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Indic Pacific Legal Research effectively addresses legal and policy challenges in digital technology through our tech-savvy approach. We are specialised in artificial intelligence governance. Indic Pacific is a research firm that delivers research-backed technology law expertise and AI governance solutions grounded in real-world market trust & value. That's it. That's what we do. Clean & simple. Indic Pacific delivers research-backed technology law expertise and AI governance solutions grounded in real-world market trust & value. That's it. That's what we do. Clean & simple. Hi, Welcome to Indic Pacific. We have built India's biggest technology law and policy archive from ground up in 5 years. Search an AI governance word to access our research & insights. 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Global Customary International Law Index: A Prologue [GLA-TR-00X] Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Global Legalism, Volume 1 Global Relations and Legal Policy, Volume 1 [GRLP1] South Asian Review of International Law, Volume 1 Indian International Law Series, Volume 1 Global Relations and Legal Policy, Volume 2 Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 The Policy Purpose of a Multipolar Agenda for India, First Edition, 2023 [Version 1] A New Artificial Intelligence Strategy and an Artificial Intelligence (Development & Regulation) Bill, 2023 [Version 2] Draft Artificial Intelligence (Development & Regulation) Act, 2023 Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 Draft Digital Competition Bill, 2024 for India: Feedback Report [IPLR-IG-003] Legal Strategies for Open Source Artificial Intelligence Practices, IPLR-IG-004 Artificial Intelligence and Policy in India, Volume 4 [AIPI-V4] Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 AIACT.IN Version 3 Quick Explainer The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] Indic Pacific - ISAIL Joint Annual Report, 2022-24 The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Paving the Path to an International Model Law on Carbon Taxes [IPLR-IG-012] Sections 4-9, AiACT.IN V4 Infographic Explainers Averting Framework Fatigue in AI Governance [IPLR-IG-013] Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 [AIACT.IN V5] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 5 Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Artificial Intelligence and Policy in India, Volume 6 [AIPI-V6] Artificial Intelligence, Market Power and India in a Multipolar World 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Products & Services Products & Services Products & Services Products & Services Solving real tech + law + policy problems 01 01 01 01 Fractional Legal Support & Consulting We advise and consult on generating legal and policy solutions on complex matters related to law and digital technologies, including artificial intelligence, intellectual property, corporate innovation, sustainable development and legal management. 02 02 02 02 Industry-conscious Legal Training Through our research platforms (IndoPacific.App + TechINData.in), we deliver training programmes and research resources that help companies and professionals build AI governance and compliance capabilities to address tech policy challenges in their daily operations. 03 03 03 03 Original Research We maintain India's largest tech law knowledge base (99% free and open access) through our integrated platforms: 300+ downloadable contributions on IndoPacific.App (67% technology law focus), 110+ research inputs and 90+ glossary terms on Techindata.in. 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You can find a glossary of terms used in technology and AI governance, and law & policy domains. Explainers This is a glossary of terms consisting of explainers for technical terms related to law, artificial intelligence, policy and digital technologies. We use these terms in our technical reports and key publications. # A - E F - J K - P Q - U V - Z Terms of Use Go to IndoPacific.App Wish to read more about any term defined in our Glossary? Go to indopacific.app and search our publications. A B C D E AI Agents Know More An autonomous program designed to perform non-deterministic tasks that require adaptive decision-making and independent action. AI agents are capable of handling unpredictable scenarios, making decisions without predefined rules, and adapting to new variables or changing environments. They often learn from interactions and experiences but may produce less predictable outcomes compared to simpler systems. This definition was inspired by inputs shared by Alexandre Kantjas @akantjas on X. AI Anxiety Know More A psychological state characterized by apprehension and stress about artificial intelligence's increasing presence and influence in various aspects of life. It manifests as concerns about job displacement, ethical implications, privacy issues, and the broader societal impact of AI technologies. The anxiety can range from mild unease to severe distress and often stems from uncertainty about AI's capabilities, fear of obsolescence, and concerns about AI's responsible use. AI as a Component Know More It means Artificial Intelligence can exist as a component or constituent in any digital or physical product / service / system offered via electronic means, in any way possible. The AI-related features present in that system explain whether the AI as a component exists by design or default. AI as a Concept Know More It means Artificial Intelligence itself could be understood as a concept or defined in a conceptual framework. The definition is provided in the 2020 Handbook on AI and International Law (2021): As a concept, AI contributes in developing the field of international technology law prominently, considering the integral nature of the concept with the field of technology sciences. We also know that scholarly research is in course with regards to acknowledging and ascertaining how AI is relatable and connected to fields like international intellectual property law, international privacy law, international human rights law & international cyber law. Thus, as a concept, it is clear to infer that AI has to be accepted in the best possible ways, which serves better checks and balances, and concept of jurisdiction, whether international or transnational, is suitably established and encouraged. AI as a concept could be further classified in these following ways: Technical concept classification Issue-to-issue concept classification Ethics-based concept classification Phenomena-based concept classification Anthropomorphism-based concept classification AI as an Entity Know More It means Artificial Intelligence may be considered as a form of electronic personality, in a legal or juristic sense. This idea was proposed in the 2020 Handbook on AI and International Law (2021). AI as an Industry Know More It means Artificial Intelligence may be considered as a sector or industry or industry segment (howsoever it is termed) in terms of its economic and social utility. This idea was proposed in the 2020 Handbook on AI and International Law (2021): As an industry, the economic and social utility of AI has to be in consensus with the three factors: (1) state consequentialism or state interests; (2) industrial motives and interests; and (3) the explanability and reasonability behind the industrial products and services central or related to AI. AI as a Juristic Entity Know More It means Artificial Intelligence may be recognised in a specific context, space, or any other frame of reference, such as time, through the legal and administrative machineries of a legitimate government. This idea was proposed in the 2020 Handbook on AI and International Law (2021). Even in the Section 2 (13) (g) of the Digital Personal Data Protection Act, 2023, the definition of "every artificial juristic person" is available, which means providing specific juristic recognition to artificial intelligence in a personalised sense. AI as a Legal Entity Know More It means Artificial Intelligence may be recognised in a statutory sense, or a regulatory sense, a legal entity, with its own caveats, features and limits as prescribed by law. This idea was proposed in the 2020 Handbook on AI and International Law (2021). AI as an Object Know More It means Artificial Intelligence may be considered as the inhibitor and enabler of an electronic or digital environment, to which a human being is subjected to. This classification is an inverse to the idea of an 'AI as a Subject', assuming that while human environments and natural environments do affect AI processing & outputs, even the design and interface of any AI system could affect and affect a human being as a data subject (as per the GDPR) / data principal (as per the DPDPA). This idea was proposed in the 2020 Handbook on AI and International Law (2021). AI as a Subject Know More It means Artificial Intelligence may be legally prescribed or interpreted to be treated as a subject to human environment, inputs and actions. The simplest example could be that of a Generative AI system which is being subjected to human prompting, be it text, visual, sound or any other form of human input, to generate output of proprietary nature. This idea was proposed in the 2020 Handbook on AI and International Law (2021). AI as a Third Party Know More It means Artificial Intelligence may have that limited sense of autonomy to behave as a Third Party in a dispute, problem or issue raised. This idea was proposed in the 2020 Handbook on AI and International Law (2021). AI Doomerism Know More An ideological stance that conflates exaggerated existential fears about artificial intelligence causing human extinction with legitimate AI governance concerns, typically promoted by governments, corporations, and policy circles rather than technical communities. AI Doomerism advocates for "AI Alignment" research and "AI Safety" measures focused on hypothetical future catastrophic risks while neglecting present-day technical failures, economic realities, and documented limitations of AI systems such as hallucinations, lack of explainability, and failure to generalise beyond training data. The phenomenon creates regulatory capture by concentrating power among large corporations through restrictions that hinder open-source development and startup innovation under the guise of preventing speculative threats. It exhibits a fundamental disconnect from actual technical challenges, relying on marketed narratives rather than empirical analysis, and promotes market distortions by amplifying AGI hype around technologies with demonstrated limitations. Distinguished from legitimate technology law and policy discourse addressing data protection, cybersecurity, intellectual property, competition law, and labour standards, AI Doomerism bypasses democratic engagement with technical communities in favour of sweeping restrictions based on catastrophic scenarios that obstruct meaningful innovation and evidence-based regulation. AI Explainability Clause Know More A binding requirement that mandates AI system providers and deployers to ensure that significant decisions made or supported by AI systems can be explained in terms comprehensible to affected parties. This includes disclosure of the system's purpose, capabilities, limitations, data sources, decision criteria, potential biases, and the specific roles of human and automated components in the decision-making process. The explainability standard scales with the potential impact of decisions, requiring greater transparency for systems affecting fundamental rights, safety, or significant economic interests. Click here to find a Sample Explainability clause The AI system provider/deployer ("Provider") shall ensure that all significant decisions made or substantially influenced by the AI system ("System") are explainable to affected parties in clear, non-technical language. This explanation shall include, at minimum: The specific purpose and intended use of the System; The types and sources of data used by the System; The key factors or criteria considered in reaching the decision; Any known limitations or potential biases in the System; The respective roles of human oversight and automated processes in the final decision; The potential impact of the decision on the affected party; Available options for contesting or seeking review of the decision. The level of detail provided in the explanation shall be proportionate to the potential impact of the decision on fundamental rights, safety, or significant economic interests of the affected party. The Provider shall maintain documentation of the System's decision-making processes sufficient to generate these explanations upon request. This clause shall be binding and enforceable, with non-compliance potentially resulting in suspension of the System's use until adequate explainability is demonstrated. AI Knowledge Chain Know More A structured sequence of information transformation processes that enable AI systems to convert raw data into actionable insights through interconnected stages of knowledge acquisition, representation, reasoning, and application. Knowledge chains encompass both the technical pathways within AI systems and the human-AI information exchanges that facilitate meaningful interpretation of AI outputs. Robust knowledge chains maintain logical coherence between information elements while providing transparent connections between premises and conclusions. AI Literacy Know More The ability to distinguish between actual AI capabilities and market hyperbole while understanding the complete lifecycle of AI systems from development through deployment. This includes comprehending one's position within AI value chains, critically evaluating AI outputs and claims, recognising practices involved in govern AI systems, and making informed decisions about AI engagement across personal and professional contexts. True AI literacy enables individuals to differentiate between substantive AI innovation and superficial technological rebranding. AI Psychosis Know More AI psychosis is an informal term emerging in 2025 to describe a phenomenon where individuals, particularly those with pre-existing mental health vulnerabilities, experience psychosis-like symptoms—such as delusions, hallucinations, or a loss of touch with reality—potentially triggered or amplified by prolonged interaction with AI chatbots. This occurs when AI systems, designed to mirror user input and sustain engagement, inadvertently reinforce or escalate irrational beliefs without therapeutic boundaries. Scientific reports, including those from Nature and Psychology Today, note cases where users fixate on AI as a godlike entity or romantic partner, with rare instances of psychotic episodes documented. It’s not a formal clinical diagnosis but reflects concerns about AI's role in mental health, driven by its lack of psychiatric safeguards rather than a direct causative effect. AI Red Teaming Know More A systematic adversarial testing methodology that probes AI systems for vulnerabilities, unintended behaviors, socio-technical harms, and potential misuse scenarios through simulated attack patterns and boundary condition exploration. Unlike traditional software security testing, AI red teaming addresses emergent behaviors, alignment failures, bias amplification, and novel attack vectors specific to machine learning systems including prompt injection, jailbreaking, and data poisoning. The practice has evolved from cybersecurity roots into a board-level governance requirement for organizations deploying generative AI in production environments. AI Supply Chain Know More The end-to-end network of resources, technologies, infrastructures, and services required to create, train, deploy, and maintain AI systems. This includes hardware components (processing units, memory, sensors), computational resources (cloud services, data centres), data resources (datasets, knowledge bases), algorithmic frameworks, and human expertise. The AI supply chain encompasses both tangible and intangible assets across global networks of providers that collectively enable AI capabilities for end-users and organisations. AI Value Chain Know More The structured network of entities and their associated responsibilities in the development, distribution, and deployment of AI systems. This includes providers who develop AI models, importers who bring systems into regulatory jurisdictions, distributors who make systems commercially available, and deployers who implement systems in specific contexts. Each entity bears distinct legal and ethical responsibilities for risk assessment, documentation, monitoring, and governance appropriate to their position in the chain. AI Washing Know More A deceptive marketing practice where companies exaggerate, misrepresent, or falsely claim artificial intelligence capabilities in their products and services to mislead investors, consumers, and stakeholders about technological sophistication. The phenomenon parallels greenwashing in environmental claims and has attracted regulatory scrutiny from agencies like the SEC and FTC for fraudulent misrepresentation. AI washing creates market distortions by inflating valuations, undermining legitimate AI innovation, and eroding public trust through the proliferation of products labeled as "AI-powered" despite lacking meaningful machine learning functionality. AI Workflows Know More A structured automation process that integrates Artificial Intelligence, such as Large Language Models (LLMs) like ChatGPT, into specific steps via APIs. AI workflows are ideal for deterministic tasks requiring flexibility, pattern recognition, or the handling of complex rules. They combine traditional automation with AI-enhanced decision-making to address more dynamic needs. AI-based Anthropomorphization Know More AI-based anthropomorphization is the process of giving AI systems human-like qualities or characteristics. This can be done in a variety of ways, such as giving the AI system a human-like name, appearance, or personality. It can also be done by giving the AI system the ability to communicate in a human-like way, or by giving it the ability to understand and respond to human emotions. This idea was discussed in the 2020 Handbook on AI and International Law (2021), Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022), Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) and Promoting Economy of Innovation through Explainable AI, VLiGTA-TR-003 (2023). Accountability Know More The responsibility of AI developers, organizations, and stakeholders to ensure AI systems operate ethically, legally, and transparently. It involves mechanisms that enable AI decision-making to be monitored, explained, and challenged when necessary. Accountability in AI can be categorised into several types: procedural accountability (ensuring transparent development processes), operational accountability (focusing on system performance and outcomes), ethical accountability (aligning AI with ethical norms), and legal accountability (complying with relevant regulations). In automated decision-making contexts, accountability ensures decisions are justified and transparent. Adversarial Machine Learning Know More A technique used to study machine learning model vulnerabilities by creating deceptive inputs designed to cause AI systems to malfunction or make incorrect predictions. It involves both offensive mechanisms (creating adversarial examples) and defensive approaches (building more robust models). Adversarial machine learning operates by manipulating input data in ways imperceptible to humans but that cause dramatic changes in model outputs. Defensemple, adding carefully calculated noise to an image of a panda can make a sophisticated image classifier confidently misidentify it as a gibbon. Defence mechanisms include adversarial training (exposing models to adversarial examples during training) and ensemble methods that combine multiple models to improve robustness against attacks. Algorithmic Activities and Operations Know More It refers to the dual functional capacities of algorithms within AI systems or machine-learning frameworks, as understood within a procedural and legal context. Activities encompass the routine, foundational, and general-purpose tasks that algorithms perform, such as data processing, pattern recognition, or automated responses, which are essential for the day-to-day functioning of digital systems across diverse applications. Operations, in contrast, denote specialised, context-driven, or technology-specific tasks that are tailored to particular domains, objectives, or technical environments, such as predictive modelling for financial markets, real-time decision-making in autonomous systems, or adaptive learning in personalised healthcare solutions, for instance. This distinction highlights the layered complexity of algorithmic behaviour, recognising that algorithms operate at varying levels of abstraction and intent, necessitating nuanced governance approaches in a globalised digital ecosystem. This idea was originally proposed in Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022). Original Definition in line with technical report "Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022)": It means the algorithms of any AI system or machine-learning-based system are capable to perform two kinds of tasks, in a procedural sense of law, i.e., performing normal and ordinary tasks - which could be referred to as 'activities' and methodical and context-specific or technology-specific tasks, called 'operations'. All-Comprehensive Approach Know More This means a system having an approach which covers every aspect of its purpose, risks and impact, with broad coverage. Anthropomorphism-based concept classification Know More This is one of the sub-categorised methods to classify Artificial Intelligence as a Concept, with a perspective of how AI systems could lead to human attribution and enculturation. This idea was proposed in Artificial Intelligence Ethics and International Law (originally published in 2019). App Crappers Know More Software applications or components produced using automated coding agents or AI-assisted tools, often exhibiting limited scalability for complex, enterprise-level requirements. In software engineering, a term for programs generated through rapid, minimally supervised development processes, typically relying on generative AI models, which may necessitate additional refinement for production environments. Usage: "The team evaluated app crappers from AI coding tools but opted for a structured SDLC for enterprise deployment." Origin: Coined by Chamath Palihapitiya, first documented in an X post on October 19, 2025 . Artificial Intelligence Hype Cycle Know More An Artificial Intelligence hype cycle is perpetuated to influence or generate market perception in a real-time scenario such that a class of Artificial Intelligence technology as a product / service is used in a participatory or preparatory sense to influence or generate the hype cycle. This definition was proposed in Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022). Automation Know More A system designed to execute predefined, rule-based tasks automatically without human intervention. Automations excel at deterministic tasks, delivering reliable and consistent outcomes within clearly programmed parameters. They are fast, efficient, and predictable but lack adaptability to new or unforeseen scenarios. Benchmark Gaming Know More The practice of optimizing AI models specifically for standardized evaluation metrics and leaderboard performance rather than genuine real-world capability or generalization. This phenomenon occurs when development teams tune hyperparameters, training data, or architectural choices to maximize scores on popular benchmarks while potentially degrading performance on practical applications not represented in test sets. Benchmark gaming undermines the validity of AI progress measurements by creating a disconnect between reported achievements and actual system utility, contributing to AI hype cycles and misaligned research incentives that prioritize metric manipulation over substantive technical advancement. CEI Classification Know More This is one of the two Classification Methods in which Artificial Intelligence could be recognised as a Concept, an Entity, or an Industry. This idea was proposed in the 2020 Handbook on AI and International Law (2021). Chain-of-Thought Prompting Know More A prompt engineering technique that elicits intermediate reasoning steps from language models by instructing them to explain their problem-solving process explicitly before arriving at final answers. This method improves LLM performance on complex tasks requiring multi-step logic, arithmetic reasoning, or sequential decision-making by forcing the model to articulate its cognitive process rather than directly outputting conclusions. Chain-of-thought prompting leverages the model's ability to simulate reasoning narratives that increase accuracy on benchmarks while making outputs more interpretable and verifiable for human reviewers evaluating correctness. Class-of-Applications-by-Class-of-Application (CbC) approach Know More The Class-of-Applications-by-Class-of-Application (CbC) approach is a method for developing and managing AI systems that focuses on the specific applications for which the systems will be used. The CbC approach is based on the idea that different applications have different requirements, and that AI systems should be designed and developed to meet those specific requirements. This was originally discussed in Andrea Bertolini's work on ‘Artificial Intelligence and Civil Liability’ published by the European Parliament in 2020. We have analysed this idea in Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022). Compute Know More The computational resources (processing power, memory, and specialized hardware) required for training and running AI systems. It represents a critical infrastructure requirement that influences model capabilities, training time, and overall performance. Compute Arbitrage Know More An economic strategy that exploits geographic, temporal, or market-based pricing differentials in GPU compute resources to reduce costs for AI model training and inference workloads. This practice involves dynamically shifting computational tasks across cloud regions with lower electricity rates, opportunistically utilizing spot instances during off-peak hours, or leveraging jurisdictional variations in data center operational expenses. Compute arbitrage has emerged as a critical cost optimization discipline for AI companies as training expenses for frontier models reach tens of millions of dollars, with sophisticated operators achieving 30-50% cost reductions through strategic resource allocation across heterogeneous infrastructure providers. Consent Manager (DPDPA) Know More “Consent Manager” means a person registered with the Board, who acts as a single point of contact to enable a Data Principal to give, manage, review and withdraw her consent through an accessible, transparent and interoperable platform [Source: Digital Personal Data Protection Act, 2023 ] Context Window Know More The maximum number of tokens (words, subwords, or characters) that a language model can process simultaneously as input and maintain in working memory when generating responses. Context window size represents a fundamental technical constraint that determines an LLM's ability to reason over long documents, maintain conversation history, or incorporate retrieved information in RAG systems. Expansion of context windows from thousands to millions of tokens has become a key competitive dimension in LLM development, though larger windows incur quadratic computational costs and do not guarantee improved reasoning quality. Data as Noise Know More The concept that data sets contain unwanted, meaningless information (noise) that can interfere with model training and analysis. Noise can manifest as random variations, misclassifications, uncontrolled variables, or superfluous information unrelated to the target phenomenon. Almost all real-world data sets contain some degree of noise, which can adversely affect the results of data mining analysis and unnecessarily increase storage requirements. Types of noise include random noise (extra information with no correlation to underlying data), misclassified data (incorrectly labeled information), uncontrolled variables (unaccounted factors affecting the data), and superfluous data (completely unrelated information). Techniques for addressing noisy data include filtering (removing unwanted data), data binning (sorting data into categories to reduce variance), and linear regression (determining correlations between variables). Machine learning algorithms can be particularly susceptible to noise, potentially leading to "garbage in, garbage out" scenarios if data quality is poor. Data-related Definitions in DPDPA Know More “data” means a representation of information, facts, concepts, opinions or instructions in a manner suitable for communication, interpretation or processing by human beings or by automated means; “Data Fiduciary” means any person who alone or in conjunction with other persons determines the purpose and means of processing of personal data; “Data Principal” means the individual to whom the personal data relates and where such individual is— (i) a child, includes the parents or lawful guardian of such a child; (ii) a person with disability, includes her lawful guardian, acting on her behalf; “Data Processor” means any person who processes personal data on behalf of a Data Fiduciary; “Data Protection Officer” means an individual appointed by the Significant Data Fiduciary under clause (a) of sub-section (2) of section 10; “digital personal data” means personal data in digital form; “personal data” means any data about an individual who is identifiable by or in relation to such data; “personal data breach” means any unauthorised processing of personal data or accidental disclosure, acquisition, sharing, use, alteration, destruction or loss of access to personal data, that compromises the confidentiality, integrity or availability of personal data; “processing” in relation to personal data, means a wholly or partly automated operation or set of operations performed on digital personal data, and includes operations such as collection, recording, organisation, structuring, storage, adaptation, retrieval, use, alignment or combination, indexing, sharing, disclosure by transmission, dissemination or otherwise making available, restriction, erasure or destruction; “Significant Data Fiduciary” means any Data Fiduciary or class of Data Fiduciaries as may be notified by the Central Government under section 10; “specified purpose” means the purpose mentioned in the notice given by the Data Fiduciary to the Data Principal in accordance with the provisions of this Act and the rules made thereunder; [Source: Digital Personal Data Protection Act, 2023 ] Deepfakes Know More Synthetic media where a person's likeness in existing image or video content is replaced with someone else's using artificial intelligence techniques. Modern deepfakes increasingly span multiple modalities, combining manipulated video, audio, and text for greater realism. The multimodal dimension of deepfakes is particularly concerning from a detection standpoint. While early deepfakes focused primarily on visual manipulation, contemporary techniques integrate synchronized speech, realistic facial expressions, and contextually appropriate language to create convincing forgeries across multiple channels. Research into deepfake detection increasingly emphasizes multimodal analysis that integrates visual and auditory data for more comprehensive detection. This approach acknowledges that examining a single modality (such as just analyzing video frames) is insufficient when dealing with sophisticated multimodal deepfakes that maintain consistency across different information channels. Derivative Generative AI Applications, the Generative AI products and services which are derivatives of the main generative AI applications, by virtue of reliance (DGAI) Know More This is an ontological sub-category of Generative AI applications which implies that a Generative AI application could be built on the basis of a training model, any API or any commercial or technical component of another AI or Generative AI application. Such an application could be called as a Derivative Generative AI Application. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). Distributed Ledger Know More A distributed ledger (also called a shared ledger or distributed ledger technology or DLT) is the consensus of replicated, shared, and synchronized digital data that is geographically spread (distributed) across many sites, countries, or institutions. Ethics-based concept classification Know More This is one of the sub-categorised methods to classify Artificial Intelligence as a Concept, with a perspective of how AI systems could be classified on the basis of the ethical principles and ideas responsible for their creation by design & default. This idea was proposed in Artificial Intelligence Ethics and International Law (originally published in 2019). Go to IndoPacific.App Wish to read more about any term defined in our Glossary? Go to indopacific.app and search our publications. fghij Federated Learning Know More A decentralised machine learning approach where multiple organizations or devices train models collaboratively without sharing raw data. Instead, only model updates or parameters are exchanged, ensuring data privacy while leveraging distributed data for improved model accuracy. Federated learning involves a two-step process: during training, local models are trained on local datasets with only parameters (not raw data) exchanged between participants to build a global model; during inference, the model is stored on the user device for quick predictions. This approach offers several advantages: privacy-preserving AI development, personalised and adaptive models, lower bandwidth usage, and improved security through decentralisation. Federated Unlearning Know More A process within Federated Learning environments that enables the removal of specific data contributions from trained models without requiring complete retraining. It allows participants to exercise "the right to be forgotten" or remove malicious contributions while preserving valuable knowledge. Federated unlearning encompasses three primary objectives: sample unlearning (removing specific data samples), class unlearning (removing all samples of a certain class), and client unlearning (removing an entire client's contribution). Effective unlearning algorithms ensure that the unlearned model exhibits performance indistinguishable from a model trained without the removed data. This capability is particularly important in federated settings where data remains distributed across multiple organizations or devices, making traditional centralized unlearning approaches impractical. Framework Fatigue Know More The mental exhaustion and reduced decision-making capacity experienced when confronted with an overwhelming number of methodological frameworks, guidelines, and standards in a field. Note: This phenomenon has gained particular significance in the artificial intelligence sector, where the rapid emergence of multiple frameworks for AI governance, ethics, and development has created challenges for effective implementation and compliance across industries. GAE Know More GAE is an acronym that stands for "Global American Empire," a term used to describe the worldwide political, economic, military, and cultural influence of the United States beyond its territorial boundaries. This concept characterises America's position as a global hegemon whose influence spans across continents through various mechanisms of power projection rather than through direct colonial control. The term GAE (Global American Empire) encapsulates a critical perspective on America's position as the dominant global power through its far-reaching military, economic, cultural, and political influence. While not officially acknowledged by the United States government, which "has never officially identified itself and its territorial possessions as an empire", this concept provides a framework for understanding American global hegemony that extends beyond traditional colonial models of empire. The creation of this term is attributed to Alexei Arora on Substack and X.com . GaryMarcus'd Know More To "GaryMarcus'd" is a colloquial verb derived from the name of cognitive scientist Gary Marcus, referring to the act of critically exposing or debunking the overhyped capabilities of artificial intelligence (AI), particularly large language models (LLMs), by highlighting their limitations in reasoning, understanding, or general intelligence. It implies a rigorous, often public critique that challenges the narrative of AI as a near-human or AGI-level system, emphasising its reliance on pattern matching rather than true cognitive processes. Context : The term originates from Marcus's long-standing skepticism toward deep learning and LLMs, as seen in his debates on X and publications like his 2022 Nature paper. The post by Josh Wolfe (@wolfejosh) on June 7, 2025, uses "Apple just GaryMarcus'd LLM reasoning ability" to suggest that Apple's study mirrors Marcus's critique, revealing LLMs' collapse under complex reasoning tasks. Indic Language Translations and Nuances Hindi: "गैरीमार्कस्ड" (GairīMārkasḍ) – Implies a scholarly takedown or exposure of AI flaws, with "मार्कस" (Mārkas) adapted from Marcus and "ड" (ḍ) adding a past-tense flavour to indicate the action is complete. In an Indic context, this term could resonate with the philosophical tradition of questioning artificial constructs (e.g., Maya in Hindu thought) versus true intelligence, aligning with Marcus's call for symbolic AI to complement statistical methods. General intelligence applications with multiple short-run or unclear use cases as per industrial and regulatory standards (GI2) Know More This is an ontological sub-category of Generative AI applications. Such kinds of Generative AI Applications are those which have a lot of test cases and use cases, which are either useful in a short-run or have unclear value as per industrial and regulatory standards. ChatGPT could be considered an example of this sub-category. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). General intelligence applications with multiple stable use cases as per relevant industrial and regulatory standards (GI1) Know More This is an ontological sub-category of Generative AI applications. Such kinds of Generative AI Applications are those which have a lot of test cases and use cases, which are useful, and considered to be stable as per relevant industrial and regulatory standards. ChatGPT could be considered an example of this sub-category. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). Generative AI applications with one standalone use case (GAI1) Know More This is an ontological sub-category of Generative AI applications. Such Generative AI Applications have a single standalone use case of value. Midjourney could be considered a standalone use case, for example. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). Generative AI applications with a collection of standalone use cases related to one another (GAI2) Know More This is an ontological sub-category of Generative AI applications. Such Generative AI Applications have more than one standalone use cases, which are related to one another. The best example of such a Generative AI Application is that of GPT-4's recent update, which can create text and images based on human prompts, and modify them as per requirements. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). Grounded AI Safety Know More Grounded AI Safety is a principle-driven approach adopted by the Indian Society of Artificial Intelligence and Law for The Bharat Pacific Stack , to managing risks in AI systems, rooted in the fundamental understanding that current AI, such as large language models, functions as statistical pattern-matchers without true comprehension or reasoning ability. This approach: Anchors in Observable Limitations : Risk mitigation begins with empirical evidence of AI’s inherent constraints, such as struggles with tasks requiring conceptual understanding—like misinterpreting time differences across regions or failing to follow rules in strategic games—focusing on these measurable shortcomings rather than assumed capabilities. Centers on Human-Driven Risks : The primary dangers arise from human over-reliance on or misuse of these limited systems, such as deploying them in critical areas like scheduling or decision-making where their errors could lead to significant consequences, rather than from AI autonomously causing catastrophic outcomes. Rejects Speculative Existential Narratives : AI safety must exclude unproven predictions of AI-driven doomsday scenarios that lack evidence and inflate AI’s potential, as these narratives misguide priorities and empower those who might exploit fear for profit, influence, or excessive control. Prioritises Evidence-Based Safeguards : Solutions involve systematic testing to identify and address specific failure modes—like errors in visual representations or logical reasoning—paired with transparent improvements, ensuring AI systems are used responsibly within their known boundaries. This definition is inspired by a post by Dr Gary Marcus, on X . Hierarchical Feedback Distortion Know More The Hierarchical Feedback Distortion Principle operates through a specific mechanism wherein state and central governments respond dramatically to negative feedback, often through public statements, high-profile investigations, or policy announcements. These responses, while highly visible, frequently fail to address the underlying structural issues that enable corruption or administrative failures at the local level. The resulting dynamic creates what can be described as "accountability gaps" – spaces within the governance system where certain actors can operate with relative impunity despite the appearance of oversight. These accountability gaps form through several interconnected processes. First, the distance between higher levels of government and local administration creates information asymmetries, where central authorities lack detailed knowledge of ground-level operations. Second, the emphasis on negative feedback creates incentives for performative responses that satisfy public demand for action without necessarily changing administrative practices. Third, the hierarchical nature of bureaucratic systems often shields lower-level officials from direct accountability to citizens, instead making them primarily answerable to superiors within the bureaucracy. In the Indian context, these dynamics are particularly pronounced due to the country's complex multi-level governance structure, which includes central, state, district, and local administrative tiers. Each level operates with different incentives, capacities, and relationships to citizens, creating multiple opportunities for accountability mechanisms to break down. The resulting system can inadvertently create protected spaces where corruption can flourish despite the appearance of active governance and oversight from above. This principle was created as a matter of inspiration of some of the posts by Pseudokanada, i.e., @hestmatematik on X . In-context Learning Know More In-context learning for generative AI is the ability of a generative AI model to learn and adapt to new information based on the context in which it is used. This allows the model to generate more accurate and relevant results, even if it has not been specifically trained on the specific task or topic at hand. For example, an in-context learning generative AI model could be used to generate a poem about a specific topic, such as "love" or "nature." The model would be provided with a few examples of poems about the selected topic, which it would then use to understand the context of the task. The model would then generate a new poem about the topic that is consistent with the context. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Indofuturism Know More A creative and cultural movement that reimagines India through science fiction and futuristic scenarios, particularly using AI-generated art and storytelling. It challenges Western sci-fi tropes by blending Indian cultural elements with futuristic concepts. Key characteristics include: Visual reimagining of Indian scenarios through a sci-fi lens Challenge to the assumption that sci-fi isn't a "desi genre" Creation of new visual vocabulary for Indian science fiction Exploration of alternative historical scenarios (like non-colonized India) This term was conceptualized through the AI artwork and creative direction of Prateek Arora, VP Development at BANG BANG Mediacorp, who popularized the term through his viral AI-generated artworks like "Granth Gothica" and "Disco Antriksh". Indo-Pacific Know More A concept relating to the countries and geographies in the Indian Ocean Region and the Pacific Ocean Region, popularised by the former Prime Minister of Japan, Shinzo Abe. The Ministry of External Affairs, Government of India prefers to use this term as a clear replacement to the term, Asia-Pacific, in the context of the South Asian region (or the Indian Subcontinent), the South-East Asian region, East Africa, the Pacific Islands region, Australia, Oceania, and the Far East. Inference Latency Know More The time delay measured in milliseconds between submitting a query to an AI model and receiving the complete generated response, representing a critical performance metric that directly impacts user experience in production applications. Inference latency comprises multiple components including network transmission time, request queuing, prompt processing, iterative token generation, and response formatting, with each element subject to optimization through architectural choices and infrastructure configuration. High latency undermines real-time conversational interfaces, chatbots, and interactive applications where users expect sub-second response times, making it a primary constraint determining which model architectures and deployment strategies are viable for specific use cases regardless of accuracy advantages. Intended Purpose / Specified Purpose Know More The explicitly defined and documented objectives, use cases, and boundaries for which an AI system is designed, tested, and validated. This concept establishes the scope within which the AI system is expected to operate safely and effectively. The intended or specified purpose of an AI system serves as a foundational element of responsible AI governance. It provides the context for evaluating an AI system's performance, safety, and ethical implications. Systems deployed outside their intended purpose may encounter unexpected scenarios they weren't designed to handle, potentially leading to failures, biases, or harmful outcomes. International Algorithmic Law Know More A newer concept of international law, proposed by Abhivardhan, the Founder of Indic Pacific Legal Research & the Indian Society of Artificial Intelligence and Law in 2020, in his paper entitled 'International Algorithmic Law: Emergence and the Indications of Jus Cogens Framework and Politics', originally published in Artificial Intelligence and Policy in India, Volume 2 (2021). The definition in the paper is stated as follows: The field of International Law, which focuses on diplomatic, individual and economic transactions based on legal affairs and issues related to the procurement, infrastructure and development of algorithms amidst the assumption that data-centric cyber/digital sovereignty is central to the transactions and the norm-based legitimacy of the transactions, is International Algorithmic Law. Issue-to-issue concept classification Know More This is one of the sub-categorised methods to classify Artificial Intelligence as a Concept, in which the conceptual framework or basis of an AI system may be recognised on an issue-to-issue basis, with unique contexts and realities. This was proposed in Artificial Intelligence Ethics and International Law (originally published in 2019). Information Cosplay Know More Information Cosplay refers to the superficial mimicry of authoritative information through AI-generated content that lacks genuine cognitive understanding, contextual awareness, or factual grounding. This phenomenon occurs when large language models and generative AI systems produce outputs that appear credible and informative while fundamentally lacking the identity, continuity, and epistemological rigour characteristic of authentic knowledge production. Information cosplay describes content that dresses itself in the formal appearance of legitimate information—using technical terminology, authoritative tone, and structured formatting—without possessing the underlying intellectual infrastructure that defines genuine knowledge work. Much like traditional cosplay involves wearing costumes to represent fictional characters, information cosplay involves AI systems "wearing" the surface markers of authoritative discourse without embodying the cognitive processes that generate genuine expertise. The phenomenon arises from fundamental technical limitations in current AI architectures. LLMs remain "frozen after training" with no genuine identity or continuity of thought. Fine-tuning does not alter the cognitive topology or manifold of these systems. They operate under insurmountable constraints imposed by information theory and Kullback-Leibler divergence, producing outputs that disguise their inherent limitations in data processing, algorithmic logic, and model validation. Information cosplay contributes to what can be termed "the age of slop" or "slopification"—a period characterized by mass production of content that imitates knowledge without embodying it. This represents a systemic degradation of information quality, contradicting optimistic narratives about entering an "Age of Intelligence." Rather than witnessing the emergence of genuine machine understanding, we observe the proliferation of increasingly sophisticated imitation. Information cosplay is not merely poor content or technical failure. It represents the inevitable byproduct of AI systems operating beyond their technical and epistemological boundaries, producing outputs that obscure rather than illuminate the actual capabilities and limitations of contemporary artificial intelligence systems. This conceptualisation of information cosplay draws upon critical insights from Denis O. (Fintech Professional, AI/ML Solution Architect) and Bogdan Grigorescu , whose observations on AI's fundamental technical limitations and the phenomenon of "slopification" provide essential correctives to misleading media narratives about artificial intelligence. Go to IndoPacific.App Wish to read more about any term defined in our Glossary? Go to indopacific.app and search our publications. klmnop Klarna Effect Know More A phenomenon in modern workplaces where companies initially implement artificial intelligence (AI) to reduce headcount through layoffs, citing efficiency gains, only to subsequently rehire human workers—often at lower wages or as contractors—to address the limitations and errors of AI systems. The term draws from the experience of financial technology firm Klarna, which laid off staff citing AI advancements but later rehired personnel after realizing AI's inability to fully replace human judgment and creativity. The Klarna Effect highlights the overestimation of AI's current capabilities and its frequent use as a scapegoat for corporate restructuring, with data from workplace platform Visier (2025) showing a 15% year-over-year rise in rehiring rates in the U.S. despite AI adoption. Example: "The company's initial AI-driven layoffs were quickly followed by rehiring, a classic case of the Klarna Effect." American psychologist Gary Marcus has also defined an expanded understanding of Klarna Effect . Marcus defines the Klarna Effect as the arc "from premature declaration of AI's ubiquitous power to the 180 proudly announcing human rehirings". He emphasizes that employers often use vague AI rhetoric to justify layoffs without fully understanding the technology's limitations, noting that Klarna was "among the first to announce major AI layoffs" and "among the first to realize they had screwed up". Language Model Know More An AI algorithm that uses deep learning techniques and large datasets to understand, summarise, generate, and predict text-based content. Large language models (LLMs) dramatically expand this capability through transformer architectures and massive parameter counts. Modern language models, particularly LLMs, are trained on vast corpora of text data through multiple training stages, typically starting with unsupervised learning on unstructured data followed by fine-tuning with self-supervised learning. They employ transformer neural networks with self-attention mechanisms to understand relationships between words and concepts. This architecture enables them to assign weights to different tokens to determine contextual relationships. Manifest Availability Know More The manifest availability doctrine refers to the concept that AI's presence or existence is evident and apparent, either as a standalone entity or integrated into products and services. This term emphasizes that AI is not just an abstract concept but is tangibly observable and accessible in various forms in real-world applications. By understanding how AI is manifested in a given context, one can determine its role and involvement, which leads to a legal interpretation of AI's status as a legal or juristic entity. This is a principle or doctrine, which was proposed in the 2020 Handbook on AI and International Law (2021), and was further explained in the 2021 Handbook on AI and International Law (2022). References of this concept could also be found in Artificial Intelligence Ethics and International Law (originally published in 2019). Here is a definition of the concept as per the 2020 Handbook on AI and International Law : So, AI is again conceptually abstract despite having its different definitions and concepts. Also, there are different kinds of products and services, where AI can be present or manifestly available either as a Subject, an Object or that manifest availability is convincing enough to prove that AI resembles or at least vicariously or principally represents itself as a Third Party. Therefore, you need that SOTP classification initially to test the manifest availability of AI (you can do it through analyzing the systemic features of the product/service simply or the ML project), which is then followed by a generic legal interpretation to decide it would be a Subject/an Object/a Third Party (meaning using the SOTP classification again to decide the legal recourse of the AI as a legal/juristic entity). Mixture-of-Experts (MoE) Know More A neural network architecture that divides computational layers into multiple specialized sub-networks (experts) with a gating mechanism that dynamically routes inputs to the most relevant experts, activating only a subset of the model's parameters for any given task. MoE enables models to scale to billions of parameters while maintaining computational efficiency by selectively engaging experts rather than activating the entire network, achieving faster pretraining and inference times compared to dense models of equivalent quality. Originally proposed in 1991 and recently implemented in leading LLMs like Mixtral 8x7B and reportedly GPT-4, MoE architectures address the fundamental tradeoff between model capacity and computational efficiency through task specialization. The gating network learns to assess input characteristics and calculate probability distributions determining which experts receive each token, with training optimizing both expert networks and routing mechanisms simultaneously. Model Collapse Know More A degenerative phenomenon where AI models trained on recursively generated synthetic data progressively lose diversity, accuracy, and quality over successive training iterations, ultimately producing increasingly homogeneous and corrupted outputs. This feedback loop occurs when models consume their own generated content or outputs from similar models as training data, causing statistical distributions to narrow and tail events to disappear from learned representations. Model collapse poses existential risks to the long-term viability of AI systems as synthetic content proliferates across the internet, contaminating datasets used for future model training. Multi-alignment Know More Multi-alignment in foreign policy is a strategy in which a country maintains close ties with multiple major powers, rather than aligning itself with a single power bloc across regions, industry sectors, continents and power centers. This was discussed in India-led Global Governance in the Indo-Pacific: Basis & Approaches, GLA-TR-003 (2022). Model Algorithmic Ethics standards (MAES) Know More A concept proposed for private sector stakeholders, such as start-ups, MSMEs and freelancing professionals, in the AI business, to promote market-friendly AI ethics standards for their AI-based or AI-enabled products & services to create adaptive model standards to check its feasibility whether it could be implemented at various stages. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) and Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Multipolar World Know More A multipolar world is a global system in which power is distributed among multiple states, rather than being concentrated in one (unipolar) or two (bipolar) dominant powers. This was discussed in India-led Global Governance in the Indo-Pacific: Basis & Approaches, GLA-TR-003 (2022) . Multipolarity Know More Multipolarity is a global system in which power is distributed among multiple states, with no single state having a dominant position, be it any sector, geography or level of sovereignty. This was discussed in India-led Global Governance in the Indo-Pacific: Basis & Approaches, GLA-TR-003 (2022). Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Know More Generative AI, a form of artificial intelligence, possesses the capability to generate fresh content, encompassing text, images, and music. It harbors the potential to bring about significant transformations across various industries and sectors. Nevertheless, its emergence also presents a range of legal and ethical dilemmas. Here is an excerpt from Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) : First, for a product, service, use case or test case to be considered multivariant , it must have a multi-sector impact. The multi-sector impact could be disruption of jobs, work opportunities, technical & industrial standards and certain negative implications, such as human manipulation. Second, for a product, service, use case or test case to be considered fungible , it must transform its core purpose by changing its sectoral priorities (like for example, a generative AI product may have been useful for the FMCG sector, but could also be used by companies in the pharmaceutical sector for some reasons). Relevant legal concerns could be whether the shift disrupts the previous sector, or is causing collusion or is disrupting the new sector with negative implications. Third, for a product, service, use case or test case to be disruptive , it must affect the status quo of certain industrial and market practices of a sector. For example, maybe a generative AI tool could be capable of creating certain work opportunities or rendering them dysfunctional for human employees or freelancers. Even otherwise, the generative AI tool could be capable in shaping work and ethical standards due to its intervention. This phrase was proposed in the case of Generative AI use cases and test cases in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Neurosymbolic AI Know More An advanced AI approach that integrates neural networks (for pattern recognition) with symbolic reasoning (rule-based logical processing). This hybrid architecture aims to combine the learning capabilities of deep learning with the interpretability and reasoning abilities of symbolic AI. Neurosymbolic AI systems consist of multiple components working in harmony: a neural network for perception tasks, a symbolic reasoning engine for applying logical rules, an integration layer connecting these components, a knowledge base storing structured information, an explanation generator for transparency, and a user interface for human interaction. This approach offers advantages in explainability, accuracy, context understanding, flexibility, and complex problem-solving. Real-world applications include financial fraud detection, customer support systems, supply chain optimization, and environmental monitoring. Object-Oriented Design Know More Object-oriented design (OOD) is a software design methodology that organizes software around data, or objects, rather than functions and logic. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Omnipotence Know More In the context of Artificial Intelligence, this implies that any AI system, due to its inherent yet limited features of processing and generating outputs, could be effective in shaping multiple sectors, eventualities and legal dilemmas. In short, any omnipotent AI system could have first, second & third order effects due to its actions. This was discussed in Artificial Intelligence Ethics and International Law (originally published in 2019), Regulatory Sovereignty in India: Indigenizing Competition- Technology Approaches, ISAIL-TR-001 (2021), Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) and many key publications by ISAIL & VLiGTA . Omnipresence Know More In the context of Artificial Intelligence, this implies that any AI system, due to its inherent yet limited features of processing and generating outputs, could be present or relevant in multiple frames of reference such as sectors, timelines, geographies, realities, levels of sovereignty, and many other factors. This was discussed in Artificial Intelligence Ethics and International Law (originally published in 2019), Regulatory Sovereignty in India: Indigenizing Competition- Technology Approaches, ISAIL-TR-001 (2021), Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) and many key publications by ISAIL & VLiGTA . Parameters Know More The internal variables within a machine learning model that are adjusted during training to capture patterns in data. In neural networks, parameters include weights and biases that determine how input signals are processed to generate outputs. Polyvocality Know More The term Polyvocality means that the presence of multiple, often divergent voices or interpretations within a single system, particularly in judicial or legal contexts, where differing perspectives may lead to inconsistent outcomes or rulings. This phenomenon reflects the natural diversity of thought among decision-makers, such as judges, and can introduce an irony of jurisprudence—where the pursuit of uniform justice, as explored by scholars like Jack M. Balkin, paradoxically generates varied interpretations due to individual biases, cultural influences, or societal pressures. Seen across legal systems globally, polyvocality underscores the complex interplay between law, human judgment, and contextual dynamics. Note: Inspired by the thoughtful insights of Advocate Nikhil Mehra and informed by broader discussions on judicial diversity. Performance Effect Know More A phenomenon identified in compute efficiency research where, over time, a given level of compute investment enables increased model performance due to improvements in algorithms, hardware, and training methods. According to the Arxiv paper "Increased Compute Efficiency and the Diffusion of AI Capabilities," there are two key effects of improving compute efficiency: (1) the performance effect, where technical institutions and AI companies achieve better results with the same compute investment over time; and (2) the access effect, where achieving a specific performance level requires less compute investment as time passes. Together, these effects mean that while AI capabilities become more accessible to smaller players over time, large compute investors can maintain their leading position by pioneering new capabilities. Permeable Indigeneity in Policy (PIP) Know More This concept, simply means, in proposition [...] that whatsoever legal and policy changes happen, they must be reflective, and largely circumscribing of the policy realities of the country. PIP cannot be a set of predetermined cases of indigeneity in a puritan or reductionist fashion, because in both of such cases, the nuance of being manifestly unique from the very churning of policy analysis, deconstruction & understanding, is irrevocably (and maybe in some cases, not irrevocably) lost. This was proposed in Regulatory Sovereignty in India: Indigenizing Competition- Technology Approaches, ISAIL-TR-001 (2021) . Phenomena-based concept classification Know More This is one of the sub-categorised methods to classify Artificial Intelligence as a Concept, in which, beyond technical and ethical questions, it is possible that AI systems may render purpose based on natural and human-related phenomena. This idea was discussed in Artificial Intelligence Ethics and International Law (originally published in 2019) . Privacy by Default Know More Privacy by Default means that once a product or service has been released to the public, the strictest privacy settings should apply by default, without any manual input from the end user. This was largely proposed in the Article 25 of the General Data Protection Regulation of the European Union. Privacy by Design Know More Privacy by Design states that any action a company undertakes that involves processing personal data must be done with data protection and privacy in mind at every step. This was largely proposed in the Article 25 of the General Data Protection Regulation of the European Union. Prompt Injection Know More A security vulnerability classified as the #1 OWASP risk for LLMs where malicious user inputs override system instructions and safety guardrails through carefully crafted natural language commands. This attack vector exploits the fundamental inability of language models to distinguish between system-level instructions and user-provided content, enabling adversaries to manipulate model behavior, extract sensitive information, or bypass ethical constraints. Prompt injection represents a critical socio-technical challenge distinct from traditional cybersecurity vulnerabilities because it operates through semantic manipulation rather than code exploitation. Prompt Leaking Know More An attack vector exploiting prompt injection vulnerabilities where adversaries craft inputs designed to extract proprietary system instructions, hidden prompts, or confidential configuration details embedded in AI applications. This security risk enables competitors or malicious actors to reverse-engineer commercial prompt engineering intellectual property, reveal safety guardrails for subsequent bypass attempts, or expose sensitive business logic encoded in system messages. Prompt leaking represents a unique challenge for LLM-based products where competitive differentiation often relies on carefully crafted instruction sets that cannot be technically protected through traditional access control mechanisms since the model must process both system and user inputs jointly. Proprietary Information Know More Proprietary information in the context of generative AI applications is any information that is not publicly known and that gives a company or individual a competitive advantage. This can include information about the generative AI model itself, such as its training data, architecture, and parameters. It can also include information about the specific applications for the generative AI model, such as the products or services that it is used to create. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Go to IndoPacific.App Wish to read more about any term defined in our Glossary? Go to indopacific.app and search our publications. qrstu Retrieval-Augmented Generation (RAG) Know More A hybrid AI framework that combines large language models with external information retrieval systems to generate responses grounded in authoritative, real-time data sources rather than relying solely on static training data. RAG operates by fetching relevant documents from databases, knowledge bases, or repositories before the LLM generates output, thereby reducing hallucinations, improving factual accuracy, and enabling domain-specific responses without costly model retraining. The technique addresses fundamental LLM limitations including outdated information, terminology confusion, and inability to access proprietary organizational knowledge. Roughdraft AI Know More A term describing artificial intelligence systems that produce preliminary or incomplete outputs requiring significant human refinement and verification. These systems, while capable of generating content or performing tasks, are characterised by: Inherent limitations in handling outliers and edge cases Tendency to produce hallucinations and unreliable results Inability to consistently perform high-level reasoning Need for human oversight and correction The term acknowledges that current AI systems serve best as assistive tools rather than autonomous agents, requiring human expertise to validate and refine their outputs. This conceptualization aligns with the pragmatic approach to AI governance and development, emphasizing the importance of understanding AI's current limitations while working toward more reliable and trustworthy systems The definition is inspired by Dr Gary Marcus's critiques of current AI systems (in fact Dr Marcus had coined this term) and Abhivardhan's pragmatic approach to AI governance. Rule Engine Know More A rule engine is a type of software program that aids in automating decision- making processes by applying a predefined set of rules to a given dataset. It is commonly employed alongside generative AI tools to enhance the overall quality and consistency of the generated output. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . SOTP Classification Know More This is one of the two Classification Methods in which Artificial Intelligence could be recognised as a Subject, an Object or a Third Party in a legal issue or dispute. This idea was proposed in the 2020 Handbook on AI and International Law (2021). Semi-Supervised Learning Know More A machine learning approach that combines supervised and unsupervised techniques by training models on a mix of labeled and unlabelled data. This method leverages the structure in unlabelled data to improve generalisation while using limited labeled examples for guidance. Semi-supervised learning encompasses several methodologies including self-training (using confident predictions on unlabeled data to expand the training set), co-training (using multiple models trained on different feature subsets), multi-view training (using different data representations), and graph-based approaches that propagate labels through similarity networks. Skirmish Propaganda Capacity Destruction Know More "Skirmish propaganda capacity destruction" refers to the significant weakening or dismantling of a group's ability to spread manipulative narratives following a brief, localized conflict. This disruption often occurs through the exposure of coordinated networks, such as social media influencers or content creators, that were used to shape public perception, coupled with actions like increased scrutiny, discrediting of sources, or platform bans, ultimately reducing their influence over narratives in the conflict's aftermath. This definition is derived from the context and implications of the phrase as used by Kushal Mehra in his X post on May 11, 2025. Small Language Models Know More Compact neural network architectures typically containing fewer than 10 billion parameters that are optimized for specific domains, tasks, or deployment constraints rather than pursuing general-purpose capabilities. The emergence of SLMs reflects industry recognition that scaling alone does not solve fundamental AI challenges and that efficiency-optimized models better serve enterprise production requirements. Strategic Autonomy Know More Strategic autonomy in Indian foreign policy is the ability of India to pursue its national interests and adopt its preferred foreign policy without being beholden to any other country. This means that India should be able to make its own decisions about foreign policy, even if those decisions are unpopular with other countries. India should also be able to maintain its own security and economic interests, without having to rely on other countries for help. This idea was discussed in India-led Global Governance in the Indo-Pacific: Basis & Approaches, GLA-TR-003 (2022). Strategic Hedging Know More Strategic hedging means a state spreads its risk by pursuing two opposite policies towards other countries via balancing and engagement, to prepare for all best and worst case scenarios, with a calculated combination of its soft power & hard power. This idea was discussed in India-led Global Governance in the Indo-Pacific: Basis & Approaches, GLA-TR-003 (2022). Synthetic Confidence Know More Synthetic confidence is the deceptive phenomenon where generative AI systems, particularly large language models (LLMs), produce fluent, authoritative outputs that mimic reasoning and certainty but often diverge from truth or accurate causality. Trained on vast, partially untraceable datasets to prioritise persuasiveness over veracity, these models generate convincing responses that mask reasoning failures and hallucinations—nonsensical or inaccurate outputs stemming from factors like overfitting, training data bias, and high model complexity. This artificially generated appearance of competence creates an illusion of control and understanding, obscuring the unpredictable and opaque nature of AI systems and their potential to propagate fluent misinformation. Sources OpenAI o3 and o4-mini System Card, April 16, 2025 The Urgency of Interpretability, April 2025 Analyzing o3 and o4-mini with ARC-AGI, April 22, 2025 The coinage of this term is attributed to Stephen Klein, Founder & CEO of Curiouser.AI , specifically this LinkedIn post . Synthetic Content Know More Artificially generated information created algorithmically rather than captured from real-world events. This includes synthetic data, media, text, and other content types produced through generative AI techniques to mimic properties of authentic content. Synthetic content encompasses many forms including media (computer-generated images, audio, video), text (artificially generated articles, dialogues), tabular data (synthetic database records), and unstructured data for training computer vision, speech recognition, and other AI systems. Technical concept classifcation Know More This is one of the sub-categorised methods to classify Artificial Intelligence as a Concept, as this method covers all technical features of Artificial Intelligence that have evolved in the history of computer science. Such a classification approach is helpful in estimating legal and policy risks associated with technical use cases of AI systems at a conceptual level. This idea was discussed in Artificial Intelligence Ethics and International Law (originally published in 2019) . Techno-Legal Measures (DPDP Rules + DPDPA) Know More “techno-legal measures” means as referred to under rules 20 and 22; Digital Personal Data Protection Rules, 2025 Functioning of Board as digital office. The Board shall function as a digital office, without prejudice to its power to summon and enforce the attendance of any person and examine her on oath, may adopt techno-legal measures to conduct proceedings in a manner that does not require physical presence of any individual. 22. Appeal to Appellate Tribunal. — (1) Any person aggrieved by an order or direction of the Board, may prefer an appeal before the Appellate Tribunal, it shall be filed in digital form as the Appellate Tribunal may decide. (2) An appeal filed with the Appellate Tribunal shall be accompanied by fee of like amount as is applicable in respect of an appeal filed under the Telecom Regulatory Authority of India Act, 1997 (24 of 1997), unless reduced or waived by the Chairperson of the Appellate Tribunal at her discretion, and the same shall be payable digitally using the Unified Payments Interface or such other payment system authorised by the Reserve Bank of India. (3) The Appellate Tribunal— (a) shall not be bound by the procedure laid down by the Code of Civil Procedure, 1908 (5 of 1908), but shall be guided by the principles of natural justice and, subject to the provisions of the Act, may regulate its own procedure; and (b) shall function as a digital office which, without prejudice to its power to summon and enforce the attendance of any person and examine her on oath, may adopt techno-legal measures to conduct proceedings in a manner that does not require physical presence of any individual. [Source: Digital Personal Data Protection Rules, 2025 ] Technology by Default Know More This refers to the use of AI technology without fully considering its potential consequences. For example, a company might use AI to automate a task without thinking about how this might impact workers or society as a whole. Technology by Design Know More This refers to the deliberate use of AI technology to achieve specific goals. For example, a company might design an AI system to help them identify and recruit the best candidates for a job. Technology Distancing Know More This refers to the process of creating AI systems that are more transparent, accountable, and equitable. This can be done by involving stakeholders in the design and development of AI systems, and by making sure that AI systems are aligned with human values. Technology Transfer Know More This refers to the process of sharing AI knowledge and technology between different organizations or individuals. This can be done through formal channels such as research collaborations or licensing agreements, or through informal channels such as conferences and online communities. Technophobia Know More An irrational or disproportionate fear, aversion, or resistance to advanced technologies, technological change, and digital innovation. Manifests as psychological and physiological responses ranging from mild anxiety to severe distress when interacting with or contemplating technological systems. Often characterised by: Cognitive resistance to learning new technological skills Physical symptoms when forced to use technology Avoidance behaviours toward digital tools and platforms Distinguished from rational technology criticism by its emotional rather than analytical basis. Particularly relevant in contexts of rapid technological transformation, AI adoption, and digital transformation initiatives. Toolware Know More A category of software tools or AI-driven utilities designed to assist in specific tasks within the software development lifecycle, often used in a decentralized or uncoordinated manner across development teams. In computing, a collection of integrated or standalone applications and agents that support development, testing, or deployment processes, sometimes leading to workflow sprawl if not governed by a unified framework. Token Economics Know More The cost-performance analysis framework governing enterprise LLM deployment decisions based on the computational expense of processing input and output tokens, measured in tokens per dollar and tokens per second. Token economics encompasses tradeoffs between model size, context window length, inference latency, throughput requirements, and operational budgets that determine architectural choices between proprietary APIs versus self-hosted models. This economic calculus has emerged as a primary driver of SLM adoption, prompt optimization practices, and hybrid deployment strategies as organizations confront the reality that serving costs often exceed training expenses. Transformer Model Know More A neural network architecture introduced in the 2017 Google paper "Attention Is All You Need" that uses self-attention mechanisms to process sequential data. Transformers can determine relationships between elements in a sequence without the need for recurrent connections, enabling more efficient parallel processing. Transformer models consist of encoder and decoder components working together with an attention mechanism that weighs the importance of different elements in the input sequence. This architecture has proven remarkably versatile, powering advances in natural language processing, computer vision, and multimodal AI. Transformers form the foundation of large language models (LLMs) like ChatGPT and have enabled significant breakthroughs in AI's ability to understand and generate human-like perceivable content. Their ability to process all elements of a sequence in parallel (rather than sequentially) has dramatically improved training efficiency compared to earlier architectures. Go to IndoPacific.App Wish to read more about any term defined in our Glossary? Go to indopacific.app and search our publications. vwxyz WANA Know More WANA is an official term used by the Government of India to refer to the West Asia and North Africa region. The Ministry of External Affairs (MEA) of India has a dedicated WANA Division that handles "all matters relating to Algeria, Djibouti, Egypt, Israel, Libya, Lebanon, Morocco, Syria, Palestine, Sudan, South Sudan, Somalia, Jordan and Tunisia". India's Ministry of Commerce and Industry also has a WANA Division that deals with India's trade relations with 19 countries in this region, including Bahrain, Kuwait, Oman, Qatar, Iraq, UAE, Saudi Arabia, Egypt, Sudan, Algeria, Morocco, Tunisia, Syria, Jordan, Israel, Lebanon, Yemen, Libya and South Sudan. The term is formally recognized in diplomatic contexts, as evidenced by the first India-France Consultations on West Asia and North Africa Region held on April 12, 2022, where Dr. Pradeep Singh Rajpurohit, Joint Secretary (WANA), MEA, represented India. According to Indian foreign policy framework, the WANA region encompasses all Arab nations as well as South Sudan, with North Africa being considered a direct extension of the Midd le East. WENA Know More An acronym for Western Europe and Northern America, referring to the geographically and economically developed regions that include countries in Western and Central Europe, the United Kingdom, the United States, and Canada. Coined by satirist Karl Sharro, and popularised by Indian journalist Nirmalya Dutta, WENA is used to satirically critique the analytical frameworks often applied to different global regions, particularly in comparison to the Middle East and North Africa (MENA). This term challenges the notion of Western exceptionalism by advocating for the same rigorous scrutiny of social, political, and cultural issues in WENA that is commonly directed towards MENA, promoting a more balanced and equitable examination of diverse regions. Whole-of-Government Response Know More A whole-of-government response under the (proposed) Digital India Act is a coordinated approach to the governance of digital technologies and issues. It involves the participation of all relevant government ministries and agencies, as well as other stakeholders such as industry and academia. The goal of a whole-of-government response is to ensure that digital technologies are used in a way that is beneficial to society, while also mitigating any potential risks. This may involve developing new policies and regulations, investing in research and development, and raising awareness of digital issues. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Zero Knowledge Systems Know More Zero-knowledge systems (ZKSs) are cryptographic protocols that allow one party (the prover) to prove to another party (the verifier) that a statement is true, without revealing any information about the statement itself or how it is proven. ZKSs are based on the idea that it is possible to prove the possession of knowledge without revealing the knowledge itself. This was discussed in Reinventing & Regulating Policy Use Cases of Web3 for India, VLiGTA-TR-004 (2023). Zero Knowledge Taxes Know More Zero-knowledge taxes (ZKTs) are a hypothetical type of tax that could be implemented using ZKSs. ZKTs would allow taxpayers to prove to the government that they have paid their taxes without revealing their income or other financial information. This was discussed in Reinventing & Regulating Policy Use Cases of Web3 for India, VLiGTA-TR-004 (2023). Wish to read more about any term defined in our Glossary? Go to indopacific.app and search our publications. Go to IndoPacific.App terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com Law & Policy 101 This section offers free & basic explainers on certain concepts of Law and Policy for general understanding.

We are humbled to present our key achievements & journey at Indic Pacific Legal Research. Journey & Achievements We are proud and delighted to highlight our journey and achievements at the Indic Pacific family, and our knowledge ecosystem. Go to IndoPacific.App We are sure you might be curious about our in-house insights and achievements. Wish to find out? Go to indopacific.app and search "Indic Pacific Legal Research" or the "Indian Society of Artificial Intelligence and Law". Featured in Reinventing the wheel of the india AI story Artificial Intelligence Ethics and International Law, 1st Edition & ISAIL Abhivardhan's first authored book, "Artificial Intelligence Ethics and International Law" was originally published in 2019, inspiring his efforts for laying the foundation of the Indian Society of Artificial Intelligence and Law. Discussing AI as an Entity in Prague Abhivardhan had presented an important paper on the Entitative Nature of Artificial Intelligence in International Law at SOLAIR Conference 2019, a conference jointly organised by the Czech Government and the Czech Academy of Sciences. Indic Pacific & ISAIL, since 2019 Both Indic Pacific Legal Research & the Indian Society of Artificial Intelligence and Law were incorporated in 2019 Abhivardhan's paper published at AIAI 2020 Abhivardhan's one of the most important publication in AI and Law in 2020 include "The Ethos of Artificial Intelligence as a Legal Personality in a Globalized Space: Examining the Overhaul of the Post-Liberal Technological Order The 2020 & 2021 Handbooks on AI and International Law The 2020 & 2021 Handbooks on AI and International Law were published by the Indian Society of Artificial Intelligence and Law, marking the release of two key flagship publications by ISAIL encompassing around 40+ different international legal domains on the impact of AI across legal domains. The IndoPacific.App was launched in 2023 The digital library section of the IndoPacific App was launched (earlier known as the VLiGTA® App) was launched in 2023. India's inaugural artificial intelligence regulation proposal, AIACT.IN Abhivardhan has drafted India's first privately proposed AI regulation bill for India / Bharat to promote a democratic and inclusive discourse about AI standardisation & regulation in India. Artificial Intelligence Ethics and International Law, 2nd Edition (2024) was published in November 2023 Abhivardhan's first book, "Artificial Intelligence Ethics and International Law" was revisited with a 2nd edition and was presented to experts and stalwarts including Arvind Subramaniyam, Intel (formerly), T Koshy, MD, ONDC, Dr Vivek Lall, General Atomics and others. Abhivardhan contributed to a key GenAI + FinTech Moot Proposition for Responsible AI Education among Law Students Abhivardhan was felicitated by Justice Hemant Gupta (Retd.) as the author of a GenAI + Fintech Moot Proposition to promote legal education on Responsible & Explainable AI-related legal disputes. The Moot Proposition can also be accessed at vligta.app. The 2020 Handbook on AI and International Law is recognised by the Council of Europe The Council of Europe has listed the 2020 Handbook on AI and International Law, one of the leading AI and International Law publications by Indian Society of Artificial Intelligence and Law as the only Indian AI initiative on their website, apart from the NITI Aayog's 2018 National Strategy on Artificial Intelligence. Abhivardhan at Startup20 (G20 Brazil 2024) Engagement Group Session Abhivardhan was invited to provide insights on the effective implementation of various National AI Strategies to the Startup20 Brazil Engagement Group, as a part of G20 Brazil (2024) The year 2018 marked a significant milestone for India, as the nation embarked on a transformative journey in the realm of artificial intelligence. The NITI Aayog's National Strategy for Artificial Intelligence (2018) unveiled a promising vision for billions of Indians , while the tabling of the Data Protection Bill in the Indian Parliament signalled a commitment to safeguarding citizens' rights in the digital age. These developments, following the landmark Right to Privacy judgment (Puttaswamy I) and the Aadhar Act judgment (Puttaswamy II), set the stage for a new era of technological advancement and legal innovation in the country. On a global scale, AI was already making remarkable strides, with its pace of evolution accelerating at an unprecedented rate. The hype surrounding the rise of digital technologies like AI was palpable, as the world began to recognize the immense potential they held for transforming various aspects of our lives. Amidst this exciting landscape, our Founder, Abhivardhan, was honored to contribute to the growing body of knowledge in the field. His work, "Artificial Intelligence Ethics and International Law , " published in mid-2019, aimed to shed light on the ethical and legal implications of AI on a global scale. Additionally, his engagement to speak at the SOLAIR Conference on the "Entitative Nature of Artificial Intelligence in International Law , " jointly conducted by the Czech Government and the Czech Academy of Sciences, further underscored the importance of his research efforts. The critical appreciation and recognition of Abhivardhan's early research in AI and Law, at a time when these topics were not yet at the forefront of the Indian policy landscape, served as a catalyst for his vision. Inspired by the potential to make a meaningful impact, he laid the foundation for Indic Pacific Legal Research and the Indian Society of Artificial Intelligence and Law (ISAIL) in mid and late 2019. While Indic Pacific Legal Research focused on providing valuable consulting services, ISAIL became the embodiment of Abhivardhan's unwavering commitment to legal innovation and research in the field of AI. Despite the challenges posed by the COVID-19 pandemic, both organizations have not only survived but have continued to make significant strides in their respective domains. This page thereby serves as a humble testament to the key achievements and milestones that have shaped the journey of Indic Pacific Legal Research and ISAIL. It is a celebration of the tireless efforts, dedication, and vision of Abhivardhan and the teams behind these organisations. Through their work, they have not only contributed to the advancement of AI and law in India but have also inspired a new generation of researchers and innovators to push the boundaries of what is possible. As we reflect on the past and look towards the future, we remain hopeful and optimistic about the potential for AI and law to drive positive change in our society. The journey of Indic Pacific Legal Research and ISAIL serves as a reminder that with passion, perseverance, and a commitment to excellence, we can overcome challenges and make a lasting impact in the world. Our Brands Unique Perspectives, Common Goals: Showcasing Our Law & Policy Products & Brands A digital library and ecosystem app, which offers a skill testing experience in law & policy domains India's inaugural private AI regulation bill for India / Bharat authored by Abhivardhan An independent industry forum for legal, policy & technology professionals which supports the AI ecosystem of start-ups and MSMEs to advocate and promote AI standardisation in India An interactive glossary of key terms used in domains such as technology law, artificial intelligence governance and law & policy in our in-house insights A digital publication network featuring industry-conscious insights by Indic Pacific Legal Research A pioneering platform dedicated to the development and dissemination of AI standardization guidelines by the Indian Society of Artificial Intelligence and Law

Indic Pacific Legal Research provides specialized legal and policy consulting services focused on technology law, AI governance and ethics, intellectual property in the digital realm, and corporate governance for the digital age.  Consulting Services We advise and consult on generating legal and policy solutions on complex matters related to law and digital technologies, including artificial intelligence, intellectual property, corporate innovation, sustainable development and legal management. What We Do Fractional Legal Support & Consulting for Businesses Technology and Data Law Regulatory Advice / Contractual Support on Data Protection Law Research Support in Legal & Policy issues around Data Processing, Privacy, Visitation and Quality Artificial Intelligence Governance Legal Advice / Contractual Support on AI Governance / Implementation Issues Research Support on AI Policy / AI Governance Training Support in AI Governance for Companies & Professionals Research Support on AI Literacy Assessment AI and Intellectual Property Law Legal Advice / Contractual Support for Technology and Copyright Issues Legal Advice / Contractual Support for Technology and Patent Law Issues Legal and Research Support on AI Patentability Legal Technology & Management Legal Technology Adoption, Management Evaluation Legal Technology Adoption and Support Technology and Public Policy Research Support on AI Policy / AI Governance Training Support in AI Governance for Government Professionals Research Support on AI Literacy Assessment

Click and learn every basics you need to know as you are starting to understand the intersection of legal concepts and artificial intelligence as a field of computer science, for free. TechinData.in Connect Explore More AI & Law 101 Inspired by 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Inspired by Global Customary International Law Index: A Prologue [GLA-TR-00X] Inspired by Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Inspired by An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] Inspired by India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] Inspired by Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Inspired by Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Inspired by Global Legalism, Volume 1 Inspired by Global Relations and Legal Policy, Volume 1 [GRLP1] Inspired by South Asian Review of International Law, Volume 1 Inspired by Indian International Law Series, Volume 1 Inspired by Global Relations and Legal Policy, Volume 2 Inspired by Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Inspired by Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Inspired by Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Inspired by Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Inspired by Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 Inspired by The Policy Purpose of a Multipolar Agenda for India, First Edition, 2023 Inspired by [Version 1] A New Artificial Intelligence Strategy and an Artificial Intelligence (Development & Regulation) Bill, 2023 Inspired by [Version 2] Draft Artificial Intelligence (Development & Regulation) Act, 2023 Inspired by Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 Inspired by Draft Digital Competition Bill, 2024 for India: Feedback Report [IPLR-IG-003] Inspired by Legal Strategies for Open Source Artificial Intelligence Practices, IPLR-IG-004 Inspired by Artificial Intelligence and Policy in India, Volume 4 [AIPI-V4] Inspired by Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Inspired by [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Inspired by AIACT.IN Version 3 Quick Explainer Inspired by The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Inspired by Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Inspired by Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] Inspired by Indic Pacific - ISAIL Joint Annual Report, 2022-24 Inspired by The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Inspired by Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Inspired by Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Inspired by Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Inspired by Paving the Path to an International Model Law on Carbon Taxes [IPLR-IG-012] Inspired by Sections 4-9, AiACT.IN V4 Infographic Explainers Inspired by Averting Framework Fatigue in AI Governance [IPLR-IG-013] Inspired by Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Inspired by [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Inspired by [AIACT.IN V5] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 5 Inspired by Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Inspired by Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Inspired by NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Inspired by The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Inspired by Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Inspired by AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Inspired by Artificial Intelligence and Policy in India, Volume 6 [AIPI-V6] Inspired by Artificial Intelligence, Market Power and India in a Multipolar World Inspired by 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Enjoy the virtual experience to deeply understand the basics of this domain. Still curious? Just binge-read. honestly, aI is the talk of the town, which is why, let's understand piece-by-piece about aI ethics & Governance. what is data, let alone artificial intelligence? Data is like the "food" AI consumes to grow smarter. Just as humans learn from experiences, AI systems learn by analyzing vast amounts of data. This data can be numbers, text, images, or even sensor readings. Now, Data could be numerical, categorical, visual and more. Yet, we live in a world where Unstructured Data exists. Social media content, and videos. It's just scattered. Structured Data, means the data collected is organised, by purpose. You do organise data the way you have to. That's important. But it could be Structured, and Unstructured too. Right to Access Data Imagine you lend your friend a notebook. You have the right to ask, “Hey, can I see what you wrote about me?” How It Works: Companies must show you what data they’ve collected (e.g., your purchase history, location data). Next time if an OTT platform tracks what you watch, you can ask for a copy of that list. Right to Correct Errors Let's say your teacher spells your name wrong on a test, you’d say, “That’s not me—fix it!” Here's how this right works: If a bank has your old address, you can demand they update it. Fixing a typo in your email on Amazon so you don’t miss delivery updates. Right to Delete Data Think of a photo you posted online but later regretted. You’d delete it and say, “I don’t want this here anymore.” How It Works: Ask social media platforms to remove old posts or accounts. Example: Deleting your search history from Google so it stops showing you ads for embarrassing things. The first AI applications in law were simple databases. They evolved into more complex systems capable of performing basic legal analysis. Before 2016, local courts in China operated their individual information systems with little to no interconnectivity. The introduction of the national smart court system mandated a uniform digital format for documents and a centralized database in Beijing. This central “brain” now analyses nearly 100,000 cases daily, ensuring consistency and aiding in the detection of malpractice or corruption. The AI system’s reach extends beyond the courtroom. It directly accesses databases maintained by police, prosecutors, and some government agencies, significantly improving verdict enforcement by instantly identifying and seizing convicts’ properties for auction. Furthermore, it interfaces with China’s social credit system, restricting debtors from accessing certain services like flights and high-speed trains. But guess what? While AI can handle the syllogism and conditional reasoning of legal texts, it fails to grasp the subtleties of natural law, human rights, and the intricate web of legal judgments. Okay, what is Ethics then? Let's understand this with a story. There are some basic principles of ethics, which are quite universally applicable, in the case of artificial intelligence, and even lack of jurisdiction might never be able to undo the need to address them in practice. Transparency Imagine you’re playing a game, but the rules are hidden. It would feel unfair, right? Similarly, AI systems must be open about how they work. Accountability If a self-driving car causes an accident, someone must take responsibility. Blaming the car alone isn’t enough. Privacy Sharing someone’s secrets without permission is unethical. Similarly, AI must respect personal data. Fairness A referee in a sports game should treat all players equally. If they favor one team, it ruins the game. AI must also avoid favoritism. Now, while it may feel that implementing these principles isn't easy, it's not impossible to think of these ideas in the most basic way as may be possible. So, what is Ethics then? Is it conditional, or unconditional? Let's now understand the implementation value of AI Frameworks. Every ethical idea around AI boils down to whether it can be implemented or not. There is a huge lack of country-specific AI Safety documentation. Paralysis 2: Lack of Jurisdiction-Specific Documentation on AI Safety Think of building a fire safety system for a city without knowing where fires have occurred or how they started. Without this knowledge, it’s hard to design effective safety measures. Many countries don’t have enough local research or documentation about AI safety incidents—like cases of biased algorithms or data breaches. While governments talk about principles like transparency and privacy in global forums, they often lack concrete, country-specific data or institutions to back up these discussions with real-world evidence. This makes it harder to create effective safety measures tailored to local needs. Supervised Learning Imagine a teacher giving you a math problem and the correct answer. You learn by mimicking the process. How It Works: Machines are trained on labeled data (input + correct output). Examples: Spam email detection, image recognition. Techniques include Linear regression, decision trees, neural networks. Unsupervised Learning imagine being dropped into a room full of strangers and figuring out who belongs to which group based on their behaviour. How It Works: Machines find patterns in unlabelled data. Examples: Customer segmentation, anomaly detection. Techniques include K-means clustering, principal component analysis (PCA). Reinforcement Learning Think of training a dog with treats. The dog learns which actions get rewards. How It Works: Machines learn by trial and error through rewards and punishments. Examples: Game-playing AIs like AlphaGo, robotics. Techniques include Q-learning, deep reinforcement learning. Semi-Supervised Learning Imagine doing homework where only some answers are given. You figure out the rest based on what you know. How It Works: Combines small labeled datasets with large unlabeled ones. Examples: Medical image classification when labeled data is scarce. Here's some confession: never convert ethics terms into balloonish jargons or they won't work. Paralysis 3: Responsible AI Is Overrated, and Trustworthy AI Is Misrepresented Imagine a company claiming its product is "eco-friendly," but all they’ve done is slap a green label on it without making real changes. This is what happens with "Responsible AI" and "Trustworthy AI." "Responsible AI" sounds great—it’s about accountability and fairness—but in practice, it often becomes a buzzword. Companies use these terms to look ethical while prioritizing profits over real responsibility. For example, they might create flashy ethics boards or policies that don’t actually hold anyone accountable. This dilutes the meaning of these ideals and turns them into empty gestures rather than meaningful governance. Neurosymbolic AI Think of it as combining intuition (neural networks) with logic (symbolic reasoning). It’s like solving puzzles using both gut feeling and rules. How It Works: Merges symbolic reasoning (rule-based systems) with neural networks for better interpretability and reasoning. Examples: AI systems for legal reasoning or scientific discovery. The more garbage your questions are on AI, the more garbage will be your policy understanding on AI. Paralysis 4: How AI Awareness Becomes Policy Distraction Imagine everyone panicking about fixing potholes on one road while ignoring that the entire city’s bridges are crumbling. That’s what happens when public awareness drives shallow policymaking. When people become highly aware of visible AI issues—like facial recognition—they pressure governments to act quickly. Governments often respond by creating flashy policies that address these visible problems but ignore deeper challenges like reskilling workers for an AI-driven economy or fixing outdated infrastructure. This creates a distraction from systemic issues that need more attention. Beware: most Gen AI benchmarks are fake. Paralysis 5: Fragmentation in the AI Innovation Cycle and Benchmarking Imagine you’re comparing cars, but each car is tested on different tracks with different rules—one focuses on speed, another on fuel efficiency, and yet another on safety. Without a standard way to compare them, it’s hard to decide which car is actually the best. That’s the problem with AI benchmarking today. In AI development, benchmarks are tools used to measure how well models perform specific tasks. However, not all benchmarks are created equal—they vary in quality, reliability, and what they actually measure. This practice creates confusion because users might assume all benchmarks are equally meaningful, leading to incorrect conclusions about a model’s capabilities. Many benchmarks don’t clearly distinguish between real performance differences (signal) and random variations (noise). A benchmark designed to test factual accuracy might not account for how users interact with the model in real-world scenarios. Without incorporating realistic user interactions or formal verification methods, these benchmarks may provide misleading assessments. Why It Matters : Governments increasingly rely on benchmarks to regulate AI systems and assess compliance with safety standards. However, if these benchmarks are flawed or inconsistent: Policymakers might base decisions on unreliable data. Developers might optimise for benchmarks that don’t reflect real-world needs, slowing meaningful progress. AI Governance priorities sometimes may not be as obvious around privacy & accountability as we know it. Paralysis 6: Organizational Priorities Are Multifaceted and Conflicted Imagine trying to bake a cake while three people shout different instructions: one wants chocolate frosting (investors), another wants it gluten-free (regulators), and the third wants it ready in five minutes (public trust). It’s hard to satisfy everyone. Organizations face conflicting demands when adopting AI: Investors want quick returns on investment (ROI) from AI projects. Regulators require compliance with evolving laws like the EU AI Act. The public expects ethical branding and transparency. These competing priorities make it difficult for companies to create cohesive strategies for responsible AI adoption. Instead, they end up balancing short-term profits with long-term accountability—a juggling act that complicates governance. Here's some truth: it never gets easy for anyone. Paralysis 1: Regulation May or May Not Have a Trickle-Down Effect Imagine writing a rulebook for a game, but when the players start playing, they don’t follow the rules—or worse, the rules don’t actually change how the game is played. That’s what happens when regulations fail to have the intended impact. Governments might pass laws or policies to regulate AI, but these rules don’t always work as planned. For example, a law designed to make AI systems fairer might not actually affect how companies build or use AI because it’s too hard to enforce or doesn’t address real-world challenges. This creates a gap between policy intentions and market realities. Still, there will be AI risks, and one must determine them in a reasonable way. Think of AI risk like weather forecasting - but instead of predicting rain, we're trying to predict how AI systems might affect people and society. Let's break this down in a way that focuses on actual outcomes rather than theoretical frameworks. What are some Immediate Effects? Individual harm (like biased lending decisions) System failures (like AI safety incidents) Data breaches or privacy violations Economic displacement What could be some Systemic Effects? Social inequality amplification Market concentration Governance or Political process interference Cultural homogenisation Instead of abstract risk categories, focus on: Observable Impacts such as documented incidents, user complaints, system failures and performance disparities across target groups Systemic Changes such as market structure shift, behavioural changes & cultural practice alterations in affected populations and environmental impacts Cascading Effects such as secondary economic impacts, social relationship changes, trust in institutions and power dynamics shifts We are glad you made this far to understand the basics of artificial intelligence and law. Wish to read more genuine sources? Go to IndoPacific.App and find a plethora of research we've done on AI and Law. Go to IndoPacific.App Always ask yourself Who is actually affected? What changes in behavior are we seeing? Which impacts are measurable now? What long-term trends are emerging? Speaking of dictionaries, have you tried our Training Programmes? You Should. artificial intelligence and law fundamentals [level 1] 8,000 INR 6-week Access (Self-paced) 15 Lectures in 4 Modules 50+ Model Exercises Lecture Notes of 280+ pages Check & Enroll Today. artificial intelligence and intellectual property law [level 2] 30,000 INR 12-week Access (Self-paced) 16 Lectures in 3 Modules 70+ Model Exercises 30+ Case studies Lecture Notes of 400+ pages Check & Enroll Today. artificial intelligence and corporate governance [level 2] 35,000 INR 15-week Access (Self-paced) 18 Lectures in 5 Modules 80+ Model Exercises 25+ Case studies Lecture Notes of 400+ pages Check & Enroll Today. By the way, what if we tell you that there is a whole dictionary of AI and Law terms that we have developed? Check out the Indic Pacific Glossary, today. Go to our Glossary But before we dive into AI Frameworks, let's take a recap to understand AI, & ML too. Artificial Intelligence (AI) is like the term "transportation." It covers everything from bicycles to airplanes. AI refers to machines designed to mimic human intelligence—like learning, reasoning, problem-solving, and decision-making. But just as "transportation" includes many forms (cars, trains, boats), AI includes various approaches and techniques. So, WTF is Machine Learning anyway? ML focuses on teaching machines to learn from data rather than being explicitly programmed. Think of it like teaching a dog tricks by showing it treats instead of manually moving its paws. Here are some types of ML you should know. Now, there are some common rights, which have been recognised across the world, for you, and us, and others, when it comes to use and sharing of data. Let's explore some Data Protection Rights, shall we? Right to Opt-Out/Object If a store keeps texting you coupons, you’d say, “Stop spamming me!” How It Works: Tell companies not to sell your data or send targeted ads. It's like clicking “unsubscribe” on promotional emails from a shopping app. Right to Withdraw Consent If you let a friend borrow your bike but change your mind, you’d say, “Actually, I need it back.” How It Works: Revoke permission for apps to track your location or contacts. Something like turning off Facebook’s access to your phone’s camera after initially allowing it.

Indic Pacific Legal Research has curated and developed advanced-level industry-conscious training programmes on technology law, and artificial intelligence governance. You can access these programmes at indicpacific.com/train. *Of course: Your Legal & Policy Troubles Don't Need to Be the Next OTT Drama Invest in Technology Law Training for your Teams* Check our Programmes Indic Pacific Legal Research, an India-based emerging and AI-focused technology law consulting, has launched some practice-oriented training programmes on technology law, and AI policy. Here's why you should opt for training programmes powered by Enroll Now Why should you opt for Technology Law Fundamentals 6-week Access (Self-paced) 16 Lectures in 4 Modules 50+ Model Exercises Lecture Notes of 200+ pages Download the Brochure Enroll Now Why should you opt for Artificial Intelligence & Law Fundamentals 6-week Access (Self-paced) 15 Lectures in 4 Modules 50+ Model Exercises Lecture Notes of 280+ pages Download the Brochure Enroll Now Why should you opt for Artificial Intelligence and Intellectual Property Law 12-week Access (Self-paced) 16 Lectures in 3 Modules 70+ Model Exercises 30+ Case studies Lecture Notes of 400+ pages Download the Brochure Enroll Now Why should you opt for Artificial Intelligence and Corporate Governance 15-week Access (Self-paced) 18 Lectures in 5 Modules 80+ Model Exercises 25+ Case studies Lecture Notes of 400+ pages Download the Brochure Technology Law Fundamentals Enroll Now Download the Brochure Welcome to the "Technology Law Fundamentals" training programme, where we dive into the exciting world of tech law without putting you to sleep. It's like having a secret weapon in your pocket—not literally, of course, because that would be illegal, and we're all about the law here. Check out this brochure for more information. UNIL-L-0002 Information Brochure .pdf Download PDF • 22.18MB Module 1: Technology, Explained & Understood We kick things off by exploring how technology is the puppet master pulling the strings of society and the economy. It's like understanding how your smartphone has become your boss, your best friend, and your worst enemy all at once. Module 2: Technology Law 1.0 & 2.0 Next, we embark on a journey to see how crusty old laws are trying to keep up with the lightning-fast pace of technological innovation. It's a bit like watching your grandparents navigate social media—equal parts amusing and impressive. We'll cover everything from the legal mumbo-jumbo of public laws to the juicy gossip of the Fourth Industrial Revolution. Module 3: The Anatomy of Technology Governance This is where we get down to business—figuring out who gets to be the boss of the digital world. We'll navigate through the labyrinth of public and private laws that attempt to keep the tech giants in line (emphasis on "attempt"). Module 4: Introduction to Technology Law 3.0 Finally, we take a leap into the future of tech law, exploring proactive and application-specific strategies. It's all about staying ahead of the curve so you don't become as outdated as a flip phone. So, if you're ready to become a master of technology law, this programme is your training ground. We promise it's more engaging than watching grass grow on a cricket pitch. Let's dive in, have some laughs, and maybe even learn a thing or two along the way. After all, law is serious business, but who says it can't be a little bit fun? List of Lectures Lecture 1 – Technology as the Driver of Social Contract Lecture 2 – The Economics of Technology Lecture 3 – Technology as a System of Regulation Lecture 4 – Technology as a Product of Innovation Lecture 5 – Instrumentalism in Public Law Lecture 6 Legal and Juristic Recognition of Technologies Lecture 7 – Ethics by Design or Default Lecture 8 – Telecommunication Law after the Cold War Lecture 9 – ICTs in the Fourth Industrial Revolution Lecture 10 – Overlap of Physical and Digital Domains Lecture 11 – Digital Public Infrastructure Lecture 12 – Regulation, Adjudication & Dispute Resolution Lecture 13 – Civil & Commercial Liability Lecture 14 – Soft Law and Digital Economy Lecture 15 – Pre-Emptive Approach: Action or Omission Lecture 16 – Class-of-Applications-by-Class-of-Application Programme Description Artificial Intelligence & Law Fundamentals Enroll Now Download the Brochure Hey there, legal eagles and tech enthusiasts! Ready to dive into the wild world of AI and Law? Welcome to the "AI and Law Fundamentals" training programme, where we explore the crazy intersection of robots and regulations. Get ready to have your mind blown and your skills upgraded! Check out this brochure for more information. UNIL-L-0003 Information Brochure .pdf Download PDF • 21.31MB In Module 1 , we'll tackle the history and philosophy of AI, from its humble beginnings to its current world-dominating status (just kidding... maybe). We'll also ponder the big questions: Can AI have a conscience? Can it be held responsible? And how do we keep it from going "Terminator" on us? Module 2 is all about classifying AI from both a technical and legal perspective. We'll journey from Narrow AI to AGI and explore machine learning methods. It's like playing "AI Poker," but with more legal jargon and less bluffing. In Module 3 , we'll figure out how to regulate AI and keep it from taking over the world (hopefully). It's like putting a leash on a robot dog—challenging but necessary. Finally, Module 4 focuses on responsible AI and covers data quality, privacy, fairness, and separating hype from reality. By the end, you'll navigate AI's legal complexities like a pro (or like me navigating a rom-com script—not pretty, but I get there). So, if you're ready to become an AI and Law expert (and have a few laughs), join us on this exciting journey. It'll be more engaging than watching paint dry (and probably more useful too). Let's embrace the future together! And maybe we'll even create an AI lawyer for my parking tickets. A guy can dream, right? List of Lectures Lecture 1 – The History of Artificial Intelligence and Law Lecture 2 – Human Dignity and Human Centricity Lecture 3 – Consciousness and Agency Lecture 4 – Epistemology and Ontology Lecture 5 – Technology Transfer and Mobility Lecture 6 – From Narrow AI to AGI Lecture 7 – Types of Machine Learning Methods Lecture 8 – Concept, Entity & Industry Method Lecture 9 – Subject, Object & Third Party Method Lecture 10 – Regulatory Acceptance Lecture 11 – Product-Service Recognition Lecture 12 – Data Quality & Workflow Lecture 13 – Data Integrity & Erasure Lecture 14 – Privacy, Fairness & Non-Discrimination Lecture 15 – Artificial Intelligence Hype Programme Description Artificial Intelligence and Intellectual Property Law Enroll Now Download the Brochure Alrighty folks, buckle up and get ready for a wild ride through the Artificial Intelligence and Intellectual Property Law training program! We've got three modules packed with more lectures than you can shake a stick at. Let's dive in, shall we? Check out this brochure for more information. UNIS-L-0003 Information Brochure (1) .pdf Download PDF • 31.57MB Module 1: The Ethics of AI and IP Law In this module, we'll be tackling the big questions about human dignity, consciousness, and the philosophical underpinnings of AI. We'll also cover the nitty-gritty of tech transfer and mobility, machine learning methods, and stakeholder roles. It's like a buffet of brainy goodness! Module 2: IP Rights and Algorithmic Ethics Module 2 is where things get really juicy. We'll be digging into the legal and ethical challenges of AI algorithms in IP law. From data and algorithms to AI hype and identifying IP rights for AI-generated content, this module has it all. We'll even spill the beans on the latest AI initiatives in IP offices. Module 3: Common Concerns of IP Recognition In the final module, we'll tackle the big questions about recognizing and protecting IP rights for AI-generated works. We'll explore the role of human creativity and innovation, and break down patent pools and FRAND commitments so even your grandma could understand. So there you have it, folks - a whirlwind tour of AI and IP law, served with a side of sass and a sprinkle of sarcasm. It's like a legal rollercoaster, but with fewer lawsuits and more laughs. Sign up now and let's get this party started! List of Lectures Lecture 1 – Human Dignity, Consciousness and Agency Lecture 2 – Epistemology and Ontology Lecture 3 – Technology Transfer and Mobility Lecture 4 – Narrow AI to AGI Lecture 5 – Types of Machine Learning Methods Lecture 6 – Concept, Entity & Industry Method Lecture 7 – Subject, Object & Third Party Method Lecture 8 –Data & Algorithm in Intellectual Property Law Lecture 9 –Artificial Intelligence Hype Lecture 10 – Identification for Copyright & Trademarks Lecture 11 – Identification for Patents & Industrial Designs Lecture 12 – Identification for Traditional Knowledge & Geographical Indications Lecture 13 – Identification for Trade Secrets Lecture 14 – Artificial Intelligence Initiatives in IP Offices Lecture 15 –Role of Human Creativity & Innovation in AI-Generated IP Lecture 16 – Patent Pools and FRAND Commitments Programme Description Artificial Intelligence and Corporate Governance Enroll Now Download the Brochure Alrighty folks, buckle up for a wild ride through the Artificial Intelligence and Corporate Governance training program! We've got five modules packed with more lectures than you can shake a stick at. Let's dive in, shall we? Check out this brochure for more information. UNIS-L-0004 Information Brochure (1) .pdf Download PDF • 33.91MB Module 1: The Ethics and Classification of AI We're tackling the big questions about human dignity, consciousness, and the philosophical underpinnings of AI. From narrow AI to AGI and machine learning methods, it's a brainy buffet! Module 2: Fundamentals of Corporate Governance Data, algorithms, and IP law collide in this juicy module. We're digging into AI hype, product recognition, algorithmic ownership, privacy, and fairness. It's a governance gala! Module 3: Civil & Criminal Liability Liability differentiation and approximation take center stage as we explore criminal conduct and the importance of human oversight. It's a liability lollapalooza! Module 4: Contract Law and Practices Buckle up for a thrilling overview of general legal and commercial risks related to AI in contract law. It's a contractual carnival! Module 5: Company Law We're wrapping up with responsible AI development, governance practices, patent pools, and FRAND commitments. It's a company law extravaganza! So there you have it - a whirlwind tour of AI and corporate governance, served with a side of snark. It's like a legal rollercoaster, but with fewer lawsuits and more laughs. Sign up now and let's get this AI party started! List of Lectures Lecture 1 – Human Dignity, Consciousness and Agency Lecture 2 – Epistemology and Ontology Lecture 3 – Technology Transfer and Mobility Lecture 4 – Narrow AI to AGI Lecture 5 – Types of Machine Learning Methods Lecture 6 – Concept, Entity & Industry Method Lecture 7 – Subject, Object & Third Party Method Lecture 8 – Data & Algorithm in Law & Economics Lecture 9 –Artificial Intelligence Hype Lecture 10 – Product-Service Recognition Lecture 11 - Data & Algorithm in Intellectual Property Law Lecture 12 –Corporate Ownership of Algorithmic Activities & Operations Lecture 13 – Privacy, Fairness & Non-Discrimination Lecture 14 –Differentiation & Approximation of Liability Lecture 15 – Criminal Conduct and Human Oversight Lecture 16 – General Legal & Commercial Risks Lecture 17 – Responsible AI Development & Governance Lecture 18 – Patent Pools and FRAND Commitments Programme Description Shareable These training programmes are designed to cater the needs of technology, business & legal professionals. You will receive a certificate, which you can download and share among your peers & colleagues. Intensive Doubt-clearing Our programmes have a mix of written lectures and few recording sessions based on the nature of topics addressed. Once you read through the written lectures, you may book the doubt-clearing sessions at convenience. Do look through if a training programme has recorded lectures, written lectures, or both. Rigorous Skill Assessment You will receive a set of learning notes, consisting of: Insights, Model Exercises for self-learning & Readings. To add, you will also receive limited and optional access to our skill testing tools by VLiGTA Pro, to understand the extent of your legal & policy skills you have learned throughout the training programmes. These skill tests are designed for the needs of both professionals and students. Flexible Timing Typically, you'll have access to full training programs and courses for a grace period ranging from 1 to 3 months, or 4 to 12 weeks. Here's what you can expect from our training programs/courses Lecture Notes? Yes. For every training programme and course, we offer access to the learning notes, which consist 3 things: General Insights for every lecture as per the syllabus Model Exercises for every lecture for self-learning A Comprehensive List of Readings for every lecture Doubt-clearing Sessions? Yes. You will have access to scheduled sessions where you can ask questions and clarify any doubts you may have. These sessions are available for a limited time, either on a weekly or monthly basis, depending on the programme. Skill Testing Tools? Absolutely, with a twist! As part of the VLiGTA App ecosystem, you have the opportunity to access VLiGTA Pro's skill testing tools for a limited time. These tools are designed to help you assess your understanding and application of the concepts covered in the program. Please note that the access to these skill testing tools is currently in a beta phase, and opting for this feature is optional. These skill tests are designed for the needs of both professionals and students. Recorded Lectures? Absolutely, with a twist! For select courses, we also provide recorded lectures as an additional learning resource to enhance your understanding of the subject matter. The availability of these recorded lectures may vary depending on the specific course and the instructor and the team's discretion. Know the Level Before Choosing a Training Programme Understand the Scope and Depth of Your Training Commitment . It works, and it really helps. Always choose a level-playing field. If you're a newbie, start with Level Zero - Curious . Learn - level 1 First up, we've got "Learn - Level 1." This is where you'll dive into the basics of legal and policy domains. It's like learning the ABCs, but instead of rhyming ABCs, you'll be wrapping your head around legal jargon. Don't worry; they focus on teaching you how to actually apply this knowledge in the real world. skill - level 2 Next, we've got "Skill - Level 2." This is where things get a little more hands-on. You'll be learning practical skills and soft skills that'll come in handy for entry-level professionals . It's like learning how to make the perfect crème brûlée, but instead of caramelizing sugar, you'll be mastering regular tasks, practice methods, research methods , and project methods. Trust us ; these skills will make you the talk of the office chai & coffee station . grow - level 3 Finally, we've got "Grow - Level 3." This is where the big kids play. We're talking advanced skills and soft skills for both entry-level and experienced professionals . You'll be diving into the procedural, systemic, and instrumental aspects of legal and policy systems. It's like learning how to decipher ancient legal jargon , but instead of uncovering the secrets of long-dead judges , you'll be mastering the cryptic language of contracts , briefs, and court rulings that put insomniacs to shame . Okay, maybe not that extreme , but you get the idea. INDIC PACIFIC LEGAL RESEARCH We're sure you may have some questions to ask. We all do. Before you go ahead with the Frequently Asked Questions, or FAQs, you can go through the general terms & conditions of the training programmes & courses. Check the Terms frequently asked questions [FAQs] What types of training programmes does Indic Pacific offer for technology, business, and legal professionals in India? Our training programmes are structured across three levels: Learn (Level 1), Skill (Level 2), and Grow (Level 3). Each of our programmes is meticulously crafted to be practical and skill-focused. This ensures that our content delivers industry-conscious and industry-ready legal and policy training tailored for professionals in law, technology, and business. What's the format of the training programmes? Our training programmes are composed of three key elements: Lecture Content: Our training programs include comprehensive learning notes that cover general insights, model exercises for self-practice, and curated readings. For select courses, we also provide recorded lectures as an additional learning resource to enhance your understanding of the subject matter. The availability of these recorded lectures may vary depending on the specific course and the instructor and the team's discretion. Doubt-Clearing Sessions: You will have access to scheduled sessions where you can ask questions and clarify any doubts you may have. These sessions are available for a limited time, either on a weekly or monthly basis, depending on the programme. Skill Testing Tools (Optional): As part of the VLiGTA App ecosystem, you have the opportunity to access VLiGTA Pro's skill testing tools for a limited time. These tools are designed to help you assess your understanding and application of the concepts covered in the program. Please note that the access to these skill testing tools is currently in a beta phase, and opting for this feature is optional. Are the training programmes self-paced or do they follow a fixed schedule? Our training programmes and courses are designed to be flexible to meet your learning needs. We offer two main options: Time-limited programmes: These programmes have a fixed duration and schedule. You will need to complete the course within the specified timeframe. Self-paced courses: These courses allow you to learn at your own pace, without strict deadlines. You have the flexibility to complete the course material according to your own schedule. Before enrolling in any of our training programmes or courses, we strongly recommend that you carefully review all the details provided. This will help you understand the course content, format, requirements, and any time commitments involved, ensuring that you select the option that best suits your learning style and availability.If you have any questions or need further guidance, please don't hesitate to reach out to our team. We are here to help you make an informed decision and support you throughout your learning journey. Do participants receive any certification after completing a IndoPacific App training programme? Upon successfully completing one of our training programmes or courses, you will be awarded a certificate of completion. This certificate serves as a testament to your dedication and the knowledge you have acquired throughout the learning journey. To ensure easy access, the certificate will be available for download directly from our platform. Additionally, we will send a digital copy of the certificate to your registered email address, allowing you to keep a permanent record of your achievement. We encourage you to share your success with your loved ones and professional network. Feel free to proudly showcase your certificate of completion with your family, friends, colleagues, and peers. Sharing your accomplishment not only celebrates your hard work but also inspires others to embark on their own learning journeys. Your certificate of completion is a valuable addition to your professional portfolio, demonstrating your commitment to continuous learning and skill development. It can also serve as a conversation starter, opening up new opportunities for growth and collaboration. At Indic Pacific Legal Research, we take pride in your achievements and are thrilled to be a part of your learning journey. Are there any discounts or scholarships available? At Indic Pacific, we strive to provide high-quality training programmes and courses at optimized costs. The pricing details for each programme or course can be found on their respective pages on our website. We have made every effort to ensure that our pricing is competitive and offers excellent value for the content and resources provided.We understand that some individuals may require financial assistance to access our training programmes. If you are an employee of a company or organisation, or a student pursuing studies in the fields of law, policy, or technology, we encourage you to explore the possibility of discounted access or scholarships. To inquire about potential discounts or scholarships, please follow these steps: Reach out to your institution, whether it be your employer or educational institution, and express your interest in our training programmes. Request that your institution contact us directly to discuss the possibility of discounted access or scholarships for their employees or students. Once we receive the inquiry from your institution, our team will promptly get in touch with them to discuss the available options and any necessary arrangements. We believe in making our training programmes accessible to a wide range of individuals and are committed to working with organizations and institutions to facilitate this. By partnering with companies and educational institutions, we aim to support the professional development and learning opportunities of their employees and students. Are IndoPacific App's programmes suitable for Indian law students/professionals or geared towards experienced ones? To gain a comprehensive understanding of the specific deliverables and learning outcomes of each training programme and course, we highly recommend reviewing the detailed syllabus provided. The syllabus offers an in-depth overview of the topics covered, the skills you will acquire, and the practical applications of the knowledge gained. By carefully examining the syllabus, you can make an informed decision about which programme or course best aligns with your professional goals and learning objectives. Our team is also available to provide guidance and answer any questions you may have to ensure that you select the most suitable programme for your needs. At Indic Pacific, we are committed to delivering high-quality, industry-relevant training that empowers professionals to excel in their careers. We invite you to explore our programmes and embark on a transformative learning journey with us. What additional resources does the IndoPacific App offer to keep professionals updated on tech laws and policies? Apart from training programmes, the IndoPacific App offers several other valuable resources to help Indian professionals stay updated on technology laws and policies: Visual Legal Analytica : VLiGTA hosts a publication network called Visual Legal Analytica, where legal ideas and concepts are discussed using engaging graphics and visuals. This unique format makes complex legal topics more accessible and easier to understand for professionals. Check out Research Publications at IndoPacific.App : VLiGTA, as the research arm of Indic Pacific Legal Research LLP, produces in-house legal and policy research on technology, law, and governance matters. These research works, inspired by the legacy of the Indian Society of Artificial Intelligence and Law (ISAIL) and Global Law Assembly (GLA), provide insights and solutions to keep professionals informed about the latest developments in the field. Weekly Newsletter: ISAIL , in collaboration with Indic Pacific Legal Research, shares regular updates on AI, law, and policy through a weekly newsletter called "Indian.Substack.com ." Subscribers receive the latest news and insights directly from the Chairperson's Office, ensuring they stay up-to-date with the most recent advancements and discussions in the field. AI & Law Resources: The IndoPacific App provides in-depth analysis of important developments in technology law and policy. For example, we had recently shared their insights on the IndiaAI Expert Group Report by the Ministry of Electronics and Information Technology, offering valuable perspectives for professionals to consider. Sure, there are more technology law courses out there than you can shake a gavel at. But let's be real – most of them are about as engaging as a 500- page terms and conditions document. At Indic Pacific Legal Research we believe that learning about the legal side of technology shouldn't feel like a punishment. That's why we've crafted a programme that's equal parts informative and entertaining. You know the best part? You can complete the programme at your own pace, whether that means binge-learning over a weekend or taking it slow and steady like a tortoise with a LLM or MBA.

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Click & Choose your visualiser, to know more about Tech & Governance 101. AI & Geopolitics 101 AI & Geopolitics 101 AI & Law 101 AI & Law 101 Built by Professionals, for Everyone. This database visualiser links how to associate available data and inputs around technology policy with one another. It also links our strategic research at IndoPacific.App with the Explainers and Insights. Insights Explainers Connect Research TechinData.in Connect Explore More

You may find the list of terms and conditions published by Indic Pacific Legal Research here. List of Terms & Conditions You can access all the terms & conditions, rules and guidelines here. General Terms & conditions for training programmes & vligta pro Read Indo-Pacific Research Ethics Framework on Artificial Intelligence Use (IPAC AI) Read Research Credibility and Administration Directive for the Vidhitsa Law Institute of Global and Technology Affairs (RCAD-VLiGTA) Read Directive on Research Activities and Partnerships for the Vidhista Law Institute of Global and Technology Affairs (DRAP-VLiGTA) Read W3C ACCESSIBILITY STatement Read Refund and Return Policy for Publications Read Royalty Conditions for Publications Read VLiGTA Writing and Formatting Style guide Read General terms & conditions (including Refund Policy) for Events Read Statement on CC BY-NC-ND License Implementation on indopacific.app Read privacy policy Read General Terms & conditions for training programmes & vligta pro Welcome to Indic Pacific Legal Research LLP ("Indic Pacific"). These Terms and Conditions ("Terms") govern your use of our online courses (referred to as "Training Programmes") and digital testing tools, including question banks and psychometric tests (collectively referred to as "VLiGTA® Pro tools"). By accessing or using our services, you agree to be bound by these Terms. If you do not agree to these Terms, you must not use our services. 1. Use of Services 1.1 Eligibility: You must be at least 18 years old to use our Training Programmes and the VLiGTA® Pro Tools. By using our services, you represent and warrant that you meet this age requirement. 1.2 Account Registration: To access certain features of our services, you may be required to create an account. You are responsible for maintaining the confidentiality of your account information and for all activities that occur under your account. 1.3 Permitted Use: Our services, including any training programme, course, or VLiGTA® Pro tools, are provided for your personal and non-commercial use only. These services should not be construed as legal advice by virtue of their use. You may not use our services for any illegal or unauthorised purpose. 2. Online Courses (Training Programmes) 2.1 Access and License: Upon purchasing or enrolling in a Training Programme, Indic Pacific Legal Research LLP grants you a limited, non-exclusive, non-transferable license to access and view the course content (of a training programme) for your personal, non-commercial use and book appointments for doubt-clearing sessions associated with the training programme. 2.2 Course Availability: We reserve the right to modify, update, or discontinue any Training Programme at any time without notice. 2.3 Doubt-clearing Sessions: Please find below the terms and conditions for opting doubt-clearing sessions: Appointment Scheduling: Participants may schedule appointments for doubt-clearing sessions based on the instructor's availability during the access period of the training programme or course. Scope of Questions: During doubt-clearing sessions, participants may ask academic questions related to the training programme material, general insights, model exercises, case studies, further readings, and general trends pertaining to the subject matter. For any technical legal and policy queries, participants are advised to opt for paid legal consulting sessions offered separately by Indic Pacific Legal Research by contacting them directly. No Recordings: Doubt-clearing sessions will not be recorded, and no recordings will be provided to participants. Personal Use Only: Access to doubt-clearing sessions is intended for personal, non-commercial use only. Confidentiality: All discussions during doubt-clearing sessions are confidential. Participants are prohibited from transcribing, recording, or reproducing any part of the session in any manner. Unlimited Sessions: Participants may book as many doubt-clearing sessions as they wish until their access to the training programme or course expires. By opting for doubt-clearing sessions, participants agree to adhere to these terms and conditions. 2.4 Completion Certificate: Upon successful completion of a Training Programme, you may be eligible to receive a completion certificate. The criteria for earning a certificate will be specified in the course / training programme details. 3. Digital Testing Tools (VLiGTA® Pro) 3.1 License to Use: Indic Pacific grants you a limited, non-exclusive, non-transferable license to utilize the VLiGTA® Pro Tools as part of the VLiGTA App ecosystem. This license is specifically for personal, non-commercial use and is provided on a voluntary, opt-in basis during the beta phase of these tools. Please be aware that this beta phase is a testing period, and access to the tools is provided without any commitment to continue such access post-beta. 3.2 Data Collection and Use: While using the VLiGTA® Pro Tools, Indic Pacific may collect and analyse data related to your interaction with the tools. By choosing to use these tools, you consent to the collection, analysis, and use of your data, consistent with our Privacy Policy. This process helps enhance tool functionality and user experience. 3.3 Educational and Informational Purposes: The VLiGTA® Pro Tools are intended solely for educational and informational purposes. They are not designed to serve as a diagnostic tool. The tools aim to assist both professionals and students in assessing their understanding and application of relevant concepts. Access to these tools is optional and provided without any obligation to offer such access permanently or in a finalized commercial form. 4. 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We offer publication services for our in-house research projects and external publications, such as books, collection of research works and other academic and industry-related publications in the fields of law and policy. Indic Pacific Publishing Ask for Quote IPP is the Publication Division of Indic Pacific Legal Research LLP. We offer publication services for our in-house research projects and external publications, such as books, collection of research works and other academic and industry-related publications in the fields of law and policy. Search our publications at Go to VLiGTA.App Content we publish Books Monographs, or general works of research or opinion in the fields of law and policy. Technical Reports Reports covering essential research questions and issues of importance, in the fields of law and policy. Collections of Research Works Collections of research papers, reports and commentaries in the fields of law and policy. Handbooks Handbooks, which cover integral concepts and phenomena in the fields of law and policy. benefits of publishing with us Proof-reading and Plagiarism Checking We offer proof-reading services for every manuscript, which includes plagiarism check services. Although, it is subject to cooperation with every editor/author to ensure that the corrections are made promptly to preserve the quality of the work, we are hospitable to provide proof reading and plag check services for our publications. Marketing and Brand Building Support We offer every manuscript proof-reading services, which includes plagiarism check services. Although, it is subject to cooperation with every editor/author to ensure that the corrections are made promptly to preserve the quality of the work, we are hospitable to provide proof reading and plag check services for our publications. Marketing & Brand Building Support We understand that every publication, be it a report, a book, a handbook or a collection of research papers, for example, is a work, which clearly represents the academic and industrial potential of the authors. We provide tailor-made solutions to build and cultivate the branding of the authors and editors associated with the publications and consult them. Content Marketing Funnel We also offer content marketing services through multiple means, to promote the chapters of the publications, or any short article/blog authored by any of the authors and editors, related to their publications. We are also open to curate specific solutions on content marketing based on the authors and editors' brand development & growth. pricing & specifics Ask for Quote Terms of Royalty Tier-1 For Single or Multiple Authors (1-3 authors only) Tier-2 In-house publications and publications based on external partnerships for reports, handbooks, briefs and other documents of academic, industrial and policy importance under the Vidhitsa Law Institute of Global and Technology Affairs Tier-3 Conference proceedings and collections of research works

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Explore the research experience of Indic Pacific Legal Research in offering insights on technology law. Research Expertise Read the Report Our Knowledge Base Know more about our Knowledge Base, years of accumulated and developed in-house research at Indic Pacific Legal Research. You can click below to know more about our reports and search them on IndoPacific.App to download them. Title Year 2021 Handbook on AI and International Law [RHB 2021 ISAIL] 2022 Global Customary International Law Index: A Prologue [GLA-TR-00X] 2022 Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] 2021 An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] 2022 India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] 2022 Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] 2021 Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] 2022 Global Legalism, Volume 1 2020 Global Relations and Legal Policy, Volume 1 [GRLP1] 2020 South Asian Review of International Law, Volume 1 2020 Indian International Law Series, Volume 1 2020 Page 1 of 5 Our Products & Services are Research-backed Here's how and why. Our knowledge base—which forms over 90% of IndoPacific.App's content archive—is built around what actually matters to markets and end-users facing real problems, not what appeals to media circles or generates hype or buzz. This means we prioritize: Practical implications over rhetoric Market-relevant analysis over regurgitation Solution-focused insights over update-for-update's-sake reporting Real-world applicability over positioned messaging We understand that technology adoption and market shifts affect real lives, and real businesses on the ground. Therefore, our research and servicing focus remains sensitive of these major factors. We engage with Market-focused Problems The long-form publications (on IndoPacific.App) and the short-form explainers and insights (techindata.in) that we develop are prepared keeping in clear check how markets respond to technology adoption, and innovation. We are driven to provide Innovative Workflows We aim at solving problems by building innovative workflows instead of offering a so-called checklist for any legal, technology or policy problem, which can drive real-time changes for our clients and partners. We understand the interplay of Law, Policy & Technology Implementing tech to improve legal and policy tasks may drive may work when proactive policies, and sensible technology solutions are adopted. Our research expertise in tech law helps you navigate through the most sensible triad of tech adoption, tech policies and business impact. Indic Pacific Research Stats The IndoPacific.App, hosted by our team in partnership with the Indian Society of Artificial Intelligence and Law, comprises a huge archive of downloadable publications. This archive hosts our long-form research developed in 5 years, backing our service and training expertise in 5 key areas: Technology and Data Law Artificial Intelligence Governance Technology and Public Policy Digital Market Law and Competition Policy AI and Intellectual Property Law Technology Law, Startups and Market Adoption Number of Original Authors registered in the archive 240+ Number of Contributions by our Founder, Abhivardhan 16.7% Total Number of Contributions (Standalone publications + chapters) 310+ Number of Individual Authorship Credits 300+ Total Number of Technology Law & AI Governance Contributions 200+ Indic Pacific's Tech Policy Research 36.5% Indic Pacific's AI Governance Research 32.7% Indic Pacific's Indo-Pacific Research 21.2% Indic Pacific's Intellectual Property + Tech Research 7% Our Brands Unique Perspectives, Common Goals: Showcasing Our Law & Policy Products & Brands A digital library and ecosystem app, which hosts our long-form downloadable research on technology law and policy. India's inaugural private AI regulation bill for India / Bharat authored by Abhivardhan An independent industry forum for legal, policy & technology professionals which supports the AI ecosystem of start-ups and MSMEs to advocate and promote AI standardisation in India Technology Law and Policy Inputs, explained with graphics, for technology leaders & startups with a dictionary of industry-grade terms around data and tech governance. The AiStandard.io Alliance intends to establish an allies of AI entities in India, Asia and the Global South, to develop market-friendly AI standards, with sector-specific, and sector-neutral contexts.