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Context Window The Indic Pacific Glossary The Complete Glossary Context Window Date of Addition 17 Oct 2025 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. Related Long-form Insights on IndoPacific.App The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Previous Term Next Term 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

Section 1 – Short Title and Commencement PUBLISHED Previous Next Section 1 – Short Title and Commencement (1) This Act may be called the Artificial Intelligence (Development & Regulation) Act, 2023. (2) It shall come into force on such date as the Central Government may, by notification in the Official Gazette, appoint and different dates may be appointed for different provisions of this Act and any reference in any such provision to the commencement of this Act shall be construed as a reference to the coming into force of that provision. Related Indian AI Regulation Sources Information Technology Act, 2000 (IT Act 2000) October 2000 National Strategy for Artificial Intelligence (#AIforAll) June 2018 Karnataka Global Capability Center (GCC) Policy 2024-2029 November 2024

Generative AI applications with a collection of standalone use cases related to one another (GAI2) The Indic Pacific Glossary The Complete Glossary 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 Generative AI applications with a collection of standalone use cases related to one another (GAI2) Date of Addition 26 April 2024 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). Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Learn More [Version 1] A New Artificial Intelligence Strategy and an Artificial Intelligence (Development & Regulation) Bill, 2023 Learn More [Version 2] Draft Artificial Intelligence (Development & Regulation) Act, 2023 Learn More [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Learn More AIACT.IN Version 3 Quick Explainer Learn More Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Learn More Sections 4-9, AiACT.IN V4 Infographic Explainers Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Previous Term Next Term

Indofuturism The Indic Pacific Glossary The Complete Glossary 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 Indofuturism Date of Addition 19 January 2025 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". Related Long-form Insights on IndoPacific.App Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More Previous Term Next Term

Section 23 – Content Provenance and Identification PUBLISHED Previous Next Section 23 - Content Provenance and Identification (1) AI systems that generate or manipulate content must establish and maintain robust mechanisms for source attribution, origin documentation, and ethical data handling. These mechanisms shall integrate technical measures, human oversight, and compliance with applicable laws to ensure transparency and accountability in the following manner: (i) Clearly document the origins of all content sources, ensuring that: (a) Sources are identified with precision, including the website, database, or platform from which data is obtained; (b) Only publicly available data or data acquired with explicit, documented consent from the data subject is utilised, where such data collection adheres to ethical practices, defined as: (i) Ensuring transparency by publicly disclosing the purpose, scope, and intended use of data collection, enabling accountability across all applications of the AI system; (ii) Complying with all applicable laws, including the Digital Personal Data Protection Act, 2023, and respecting the terms of service, intellectual property rights, and access restrictions of data sources, to safeguard the integrity of content generation and manipulation processes; (iii) Avoiding the collection of sensitive personal data unless strictly necessary, legally permitted, and subject to heightened safeguards, including mandatory risk assessments for applications involving high-stakes decision-making or vulnerable populations; (iv) Implementing measures to prevent unauthorized access, use, or distribution of the collected data, including the use of anonymisation or pseudonymisation techniques to minimize the risk of re-identification, where: (a) Anonymisation refers to the irreversible process of transforming data into a form where the data subject cannot be identified, meeting standards of irreversibility as per best practices; (b) Pseudonymisation refers to replacing identifying characteristics with artificial identifiers, ensuring that re-identification is only possible with additional, securely stored information; (v) Permitting the use of in-copyright works for text and data mining (TDM) purposes, provided that: (a) The TDM is conducted for non-commercial research, statistical, or operational optimization purposes, supporting innovation while respecting the rights of content creators; (b) The entity has lawful access to the data, either through public availability, consent, or authorised licensing; (c) The TDM process does not involve the reproduction or distribution of the original copyrighted works beyond what is necessary for the mining process, and appropriate attribution is provided where feasible; (vi) For AI systems deployed in strategic sectors under applicable regulations, additional compliance with sector-specific data security and national interest requirements shall apply, as prescribed by the relevant authority. (c) Any use of web scraping adheres to the target website’s terms of service and robots.txt protocols, with prior written permission obtained where required. (ii) Maintain comprehensive and auditable technical documentation of data collection methods used in training datasets, which shall include: (a) A detailed description of acquisition techniques, such as APIs, manual collection, or automated scraping, ensuring all methods comply with legal and ethical standards; (b) Evidence of compliance with the Digital Personal Data Protection Act, 2023, for any personal data collected, including records of user consent where applicable; (c) A commitment to data minimization, ensuring that only data necessary for the specified purpose is collected and processed. (iii) Establish and maintain verifiable records of data provenance, categorizing data as follows: (a) Personal data, processed strictly in accordance with the Digital Personal Data Protection Act, 2023, with documented consent and purpose limitation; (b) Non-personal data, collected through authorized and transparent methods, ensuring no violation of intellectual property rights or website terms of service; (c) Synthetic data, generated by the AI system itself, with clear documentation of the generation process to distinguish it from real-world data and prevent misrepresentation. (2) Accountability for tracking AI-generated content shall be determined by the specific use cases of the AI system, such that for end-users and business end-users of AI systems, accountability and liability for AI-generated content must be examined based on factors such as: (i) Whether they intentionally misused or tampered with the AI system despite being aware of its key limitations; (ii) Whether they failed to exercise reasonable care and due diligence in the utilisation of the AI system; (iii) Whether they knowingly propagated or disseminated AI-generated content that could cause harm; (3) Intermediaries that host, publish, or make available AI-generated content shall: (i) Implement non-discriminatory content policies that: (a) Prohibit demonetisation or de-prioritisation of content solely based on its AI-generated nature when properly watermarked and disclosed; (b) Maintain parity in content recommendation algorithms between human-created and AI-generated works meeting provenance requirements; (c) Provide appeal mechanisms for creators affected by automated moderation of AI-generated content; (4) Watermarking techniques must incorporate machine-readable metadata containing: (i) Scraping methodology classification; (ii) Geographic origin of training data sources; (iii) Licensing status of underlying datasets; (5) Developers, owners, and operators of AI systems as described in sub-sections (3) to (7) of Section 6 shall obtain and maintain adequate liability insurance coverage proportionate to their commercial classification and risk profile. The coverage must include: (i) Professional indemnity insurance to cover incidents involving inaccurate, inappropriate or defamatory AI-generated content; (ii) Cyber risk insurance to cover incidents related to data breaches, network security failures or other cyber incidents involving AI-generated content; (iii) General commercial liability insurance to cover incidents causing third-party injury, damage or other legally liable scenarios involving AI-generated content; (v) Specific coverage for claims arising from data scraping activities conducted in the development, training, or operation of the AI system. (6) Exceptions for AI-Preview (AI-Pre) Systems: AI systems as described in sub-section (8) of Section 6 shall be exempt from sub-section (5) requirements only if: (i) User base remains below 50,000 real-time active testers (ii) No personal/sensitive data processing occurs (iii) Annual development budget remains under ₹5 crore (iv) System displays prominent "Preview Version" watermarks (v) Revenue generation is limited to subscription fees for testing purposes, nominal one-time access fees, or cost recovery mechanisms that do not constitute full commercial deployment, provided that: (a) Such revenue does not exceed 15% of the developing entity's total annual revenue (b) All monetary transactions are clearly disclosed as supporting a preview or test version (c) No claims of complete or commercial-grade functionality are made in marketing materials (vi) The system is not used to generate, simulate, or manipulate user consent for any purpose (vii) All interactions regarding terms of service, permissions, or agreements are conducted without AI intermediation (viii) Regular checks or audits verify the system's inputs and outputs do not engage in preference or opinion manipulation (ix) The developer maintains comprehensive logs of all system prompts and responses that could influence user decision-making (x) Users are explicitly informed if the system utilises persuasive or preference-shaping techniques in its responses (xi) Educational implementations, provided that content generation capabilities are supervised; (xii) Research applications, provided that in the case of research institutions, centres and firms: (a) Limited usage by verified research entities; (b) Publication of findings adheres to responsible disclosure guidelines; (c) Basic insurance coverage for potential third-party effects is maintained. (xiii) Terms and conditions are easily accessible in clear and plain language, and a readily contactable person is designated in accordance with sub-sections (9) and (28) of Section 2 of the Consumer Protection Act, 2019, to handle user queries, complaints, or grievances. (xiv) Appropriate insurance is maintained for any public-facing implementations. (7) AI systems as described in sub-section (8) of Section 6 exceeding any criteria in (6) must: (i) Obtain insurance within 30 days of threshold breach (ii) Reclassify under appropriate Section 6 commercial category (8) The minimum insurance coverage required for AI content generation systems shall be: (vi) ₹ 50 crores for AI-S (Artificial Intelligence as a System) and AI-IaaS (Artificial Intelligence-enabled Infrastructure as a Service) under sub-sections (6) and (7) of Section 6 respectively (vii) ₹ 25 crores for AI-Pro (Artificial Intelligence as a Product) and AIaaS (Artificial Intelligence as a Service) under sub-sections (3) and (4) of Section 6 respectively (viii) ₹ 10 crores for AI-Com (Artificial Intelligence as a Component) under sub-section (5) of Section 6 (ix) ₹ 2 crores for AI-Pre (Artificial Intelligence for Preview) under sub-section (8) of Section 6 with public-facing implementations (9) The IAIC shall establish and maintain a public registry of open-access technical methods to identify and examine AI-generated content, accessible to end-users, business users, and government officials. This registry shall provide clear instructions for using these methods and information on their validity; (10) This Section shall apply to all AI systems that generate or manipulate content, regardless of the content’s purpose or intended use, including AI systems that generate text, images, audio, video, or any other forms of content. Related Indian AI Regulation Sources Amitabh Bachchan v. Rajat Nagi & Ors., CS(COMM) 819/2022, Delhi High Court, Order dated November 25, 2022 November 2022 Advisory on Ethical Use of Social Media and Deepfakes in Elections May 2024 Advisory on Labeling AI-Generated and Synthetic Content in Elections January 2025 Karan Johar v. India Pride Advisory Private Ltd. & Ors. ("Shaadi Ke Director Karan Aur Johar"), COM IPR Suit (L.) No. 17863/2024, Bombay High Court, Order dated March 7, 2025 March 2025 Akkineni Nagarjuna v. www.bfxxx.org & Ors., CS(COMM) 1023/2025, Delhi High Court, Order dated September 25, 2025 September 2025 Aishwarya Rai Bachchan v. Aishwaryaworld.com & Ors., CS(COMM) 956/2025, Delhi High Court, Order dated September 9, 2025 September 2025 Suniel V Shetty v. John Doe & Ashok Kumar, COM IP Suit (L) No. 32130/2025, Bombay High Court, Order dated October 10, 2025 October 2025 Advisory on Enhanced Standards for AI-Generated and Synthetic Content in Elections (Bihar Assembly Elections) October 2025 Sudhir Chaudhary v. Meta Platforms Inc & Ors., CS(COMM) 1089/2025, Delhi High Court, Order dated October 10, 2025 October 2025 Hrithik Roshan v. Ashok Kumar/John Doe & Ors., CS(COMM) 1107/2025, Delhi High Court, Order dated October 15, 2025 October 2025 Raj Shamani v. John Doe & Ors., CS(COMM) 1233/2025, Delhi High Court, Order dated November 21, 2025 November 2025 Dr. Shashi Tharoor v. Ashok Kumar & Ors., CS(COMM) No. 445/2026 May 2026

Explore the fundamentals that connect true AI innovation with geopolitical strategy. Understand the languages both communities speak, the priorities that drive their decisions, and why bridging this divide matters for the future of AI governance. TechinData.in Connect Explore More AI & Geopolitics 101 Inspired by South Asian Review of International Law, Volume 1 Inspired by Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Inspired by Paving the Path to an International Model Law on Carbon Taxes [IPLR-IG-012] Inspired by Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Inspired by Indian International Law Series, Volume 1 Inspired by Global Relations and Legal Policy, Volume 2 Inspired by Global Relations and Legal Policy, Volume 1 [GRLP1] Inspired by Global Legalism, Volume 1 Inspired by Global Customary International Law Index: A Prologue [GLA-TR-00X] Inspired by Averting Framework Fatigue in AI Governance [IPLR-IG-013] Inspired by Artificial Intelligence, Market Power and India in a Multipolar World Inspired by AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Inspired by 2021 Handbook on AI and International Law [RHB 2021 ISAIL] 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. Let's be honest — the mix of geopolitics and technology is cinema. Peak cinema. But not in the sense of spectacle or fiction. It May seem dense, as if you need jargons. What if we say it is not the case? What is geopolitics for LLMs or any AI? Here's the thing: engineers speak in models and datasets. Diplomats speak in treaties and strategic interests. When they're in the same room, they're often speaking past each other — one worried about algorithmic bias, the other about algorithmic hegemony. Same problem, different vocabulary. And yet AI doesn't develop in a vacuum. Every algorithm trained reflects a technical worldview. It does not need to ultimately have a socio-political view. Yet, market desperation, political posturing, marketing tactics, and manipulation of intellectual property laws create policy friction. At least some of the above mentioned reasons of friction cause the geoeconomic dead-end. It's not entirely political, but it gets there. In short, the tech and geopolitics bubbles speak their own languages, and patterns, making 0 sense. Still, what's "political" about the geopolitics of AI? Nothing? Is it politically different to exert control over specific kinds of AI, by involving bit-by-bit, too specific rules? Is it politically common to choke resources, or talent around AI, whether through companies, or government bodies (soft law or hard law)? If both are yes, then is the geopolitics of AI not about resources and not "politics"? Contrary to popular perception, the economics of compute (semiconductors) affects mobile manufacturing, gaming ecosystems, everything - so why is it mixed with the AI side to call it "the politics of AI and compute"? No, resource economics is not why the geopolitics of AI exists, today. It isn't just a business issue. If saving domestic (and local) constituencies is why most nations actually regulate (even China - yes, even them), then for whatever additional cause - cybersecurity, human rights, business mobility, financial security, etc., why should no nation regulate at all? Or has the idea of warfare changed that all these soft power areas like constitutional morality, and regulatory sovereignty have become "weaponised"? Always remember, if everything is weaponised, then nothing is weaponised, or at least everything is not weaponised like a Kafka-esque multiverse. Some things are weaponised, while some are pushed and pulled around, creating patterns that may give a helicopter view that we all are screaming around as nations around AI or data. Deep down, some answers protect our sanity, around the resource and financing "loops" - while some times, the political positioning is merely a 20th-century or Cold War period-style positioning for dominant powers - both China and the US. Hence, the averages of sane decision-making with some percentiles of insane distortion-enabling political and regulatory decisions kind of can explain the geopolitics of AI, provided we stick to the understanding of AI & geopolitics by limiting it to a few things: The algorithmic infrastructure The trajectories of evolution for different kinds of automation The potential of scientific heuristics and ethics in defining how two kinds of ethics of AI define positions of power: The science behind the ethics of data outflow, and algorithmic infrastructure The market ethics of products, services & infrastructure built around the "AI" system. You see - this is why laws which attack the science of AI to burden regulations around systems, fail against those which target productisable, serviceable, gullible deliverables based on an ideology of regulation. Now that ideology can be Confucianism, Gandhianism, Reaganism, Putinism, or even the Great Bauhaus of the European Union. Always look for the beauty of geopolitics, not just "resource geoeconomics". Individualistic Sovereignty Imagine you write a letter. Someone else reads it, makes copies, sells information about what you wrote, and you have zero say in any of this. Digital Sovereignty is fundamentally about YOUR right to control your own data, your own digital identity, and your own choices online. Of course, when some legal rights are limited, you see country-wise deviations across. How It Works: Every time you use an app, website, or AI tool, you generate data. Digital sovereignty means you—the individual—should have the power to decide what happens to that data. Can companies surveil you? Can foreign governments access it? Can it be sold without your consent? Should you trust in national courts to handle your grievances or another glorified North American senators briefing on tech companies? Ask yourself. Normative Emergence Imagine a few neighbours start composting in their backyards. Others notice, copy them. Soon it's the neighborhood norm. Eventually, the city makes it an official rule. Normative Emergence is when technical practices or informal behaviors gradually become accepted norms, and then sometimes become formal rules. How It Works: In 1994, a Netscape engineer invented cookies as a simple technical hack—just a way to remember items in a shopping cart between page loads. It was purely practical. No policy discussion. No debate. Just code. Other developers saw it, copied it, and started using cookies for their own sites. Within a few years, advertisers discovered they could use "third-party cookies" to track users across multiple websites. By the early 2000s, cookie-based tracking became the invisible foundation of online advertising. Every ad network, every recommendation system, every personalization engine assumed cookies existed and that tracking users across sites was just "how things work". Normative Evasion Imagine your local store sells plastic bags, but the neighboring town bans them. The store simply sets up shop a few meters across the border and keeps selling. Regulatory Arbitrage is when tech companies exploit differences in national laws to continue the same practices under friendlier jurisdictions. How It Works: AI companies locate their data centers, R&D hubs, or headquarters in regions with weaker compliance regimes. This allows them to test, scale, or monetize controversial AI systems—like surveillance analytics or data-intensive recommender algorithms—without violating stricter laws elsewhere. In AI, this means systems banned in one region (e.g., EU’s high-risk classification) can still be trained offshore, then imported as models or services under a different legal label. The result? Normative evasion—a race to the bottom where frameworks exist, but enforcement gaps make them meaningless. Okay, what is Ethics then? Let's understand this. You can say, a kind of shared vocabulary that forces engineers and policymakers to stop pretending they live on different planets. 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 Tech sees it as "can you reproduce the results?" Geopolitics sees it as "who gets to see the process?" They're not arguing—they literally mean different things by the same word. Accountability If an AI agent lacks technical reliability, should those who experimented it should be made as an example of "accountability" so that nobody cares to work on technical guardrails? Also, technical accountability sometimes can have economic consequences, if not legal. But markets have been hurt. What to do then? Privacy Tech thinks privacy is solved when data is encrypted or anonymized—a technical problem with a technical fix. Geopolitics sees privacy as "who has access and under what legal authority?"—a sovereignty problem. Engineers say "we secured the database." Diplomats ask "but which government can subpoena it?" Both think they're protecting you; neither realizes the other's solution doesn't address their threat model. Fairness Tech measures fairness as statistical parity across test sets—demographic groups getting equal error rates, equal opportunity, calibrated probabilities. Geopolitics asks "fair according to whom?" One jurisdiction defines discrimination by disparate impact (outcomes), another by disparate treatment (intent), and a third doesn't recognize the category at all. 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. Let's also ask this. Do you need ethics to understand these tech & geopolitical bubbles? Absolutely. Ethics isn’t about being moral here. It’s about translating between two dialects that don’t align — one coded in math, the other in diplomacy. When technologists and policymakers talk about “values,” they’re both describing control, just through different mediums. 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. 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. 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. 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. 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. 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 geopolitical issues around AI, and one must determine them in a reasonable way. Start with data, and what kind of stakeholders would you need who create that resource equation. However, the funniest (yes, funniest) aspect of AI and geopolitics is that a typical "geoeconomic" or "economic" nexus or equation will try giving a vibe of geopolitical tensions. However, we live in a soft law world, where international rules bend more and might not be binding at all. Another problem that may emerge is how 20th-century-based heuristics and wisdom be applied to understand the "geopolitical game", even if Systemic Effects exist such as: 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 behaviour are we seeing? Which impacts are measurable now? What long-term trends are emerging? What "geopolitical" or "geoeconomic" nexus emerging is specific to 1 kind of automation, and what is truly general enough? Is it some old wine in a new bottle, legally, politically, economically or technologically? But as we have dived into AI & Geopolitics, let's take a recap to understand AI, & ML too. 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. 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. 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 our dictionary, today. Go to our Glossary So, WTF is Machine Learning anyway? Now, there are some basic concepts around artificial intelligence and geopolitics, which have stood the test of time even before the widespread use of large language models and former UK PM Boris Johnson's "chatgibbiti". 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. Benchmark Capture Imagine a university ranking that suddenly defines "success" only by test scores—but guess who makes the test? The same institutions that dominate the rankings. Benchmark Capture is when large players dictate the metrics used to judge AI reliability, safety, or fairness—creating evaluation systems they’re already optimized to win. How It Works: As Abhivardhan shows in his work on Normative Emergence , LLMs—despite being unreliable—have become the benchmark reference for all AI evaluation (citing Narayanan & Kapoor 2024; Eriksson et al. 2025). OpenAI, Anthropic, Google, and others create their own tests of factual accuracy or reasoning, but these tests aren’t scientifically grounded or cross-domain verified. Smaller AI systems, or non-LLM architectures like symbolic AI or hybrid systems, are judged by standards not made for them. This normative contagion locks the field into one family of architectures and misrepresents what “safe” or “trustworthy” AI actually means. Perception Dysmorphia Imagine looking in a mirror that distorts your reflection—making you see yourself as either bigger or smaller than you actually are. You make decisions based on that warped image, not reality. Perception Dysmorphia in AI governance is when policymakers, companies, and the public develop a fundamentally distorted view of what AI can do , what risks it poses, and whether governance measures are actually working—leading to regulations built on illusions rather than evidence. How It Works: Large Language Models like ChatGPT have created a false consensus about AI capabilities. Because LLMs can write fluently and mimic reasoning, people assume they're reliable, general-purpose intelligence systems. Governments then create governance frameworks based on LLM behavior—focusing on "hallucinations," "transparency," and "explainability"—and apply these norms to all AI systems, even ones that work completely differently (like computer vision, robotics, or symbolic reasoning systems). This creates a triple distortion: Overestimation: Policymakers think LLMs are more capable and trustworthy than they actually are, so they deploy them in high-stakes settings (legal advice, medical diagnosis, government services) without adequate safeguards. Misapplication: Governance frameworks designed for one type of unreliable AI (LLMs) get imposed on fundamentally different AI architectures that don't share those flaws—creating regulatory mismatch. Gatekeeping by Design: Compliance costs and bureaucratic requirements favor centralized AI labs with massive resources. Meanwhile, decentralized AI communities—independent developers, open-source contributors, federated learning networks—get crushed under regulations, market pressure, peer pressure, costs and maybe confusion they can't afford to manage. The real future of AI will be "extremely distributed, or largely federalised"—with innovations coming from engineering students, small research teams, and open-source communities, not just tech giants. But when governance is designed around Big Tech's LLMs, these distributed innovators face impossible barriers: they can't hire compliance officers, can't afford safety audits designed for billion-dollar models, and can't compete with incumbents who helped write the regulations.

Indian Copyright Office's controversial 2020 registration listing AI as co-author of artwork, later questioned through November 2021 withdrawal notice challenging non-human authorship. Application No: L-9 Raghav Artificial Intelligence Painting App, Indian Copyright Office (Ankit Sahni & RAGHAV AI Copyright Registration) Indian Copyright Office's controversial 2020 registration listing AI as co-author of artwork, later questioned through November 2021 withdrawal notice challenging non-human authorship. Previous Next The AIACT.IN India AI Regulation Tracker This is a simple regulatory tracker consisting all information on how India is regulating artificial intelligence as a technology, inspired from a seminal paper authored by Abhivardhan and Deepanshu Singh for the Forum of Federations, Canada, entitled, "Government with Algorithms: Managing AI in India’s Federal System – Number 70 ". We have also included case laws along with regulatory / governance documents, and avoided adding any industry documents or policy papers which do not reflect any direct or implicit legal impact. November 2020 (granted); November 2021 (withdrawal notice issued) Read the Document Issuing Authority Indian Copyright Office Type of Legal / Policy Document Judicial Pronouncements - Administrative Decisions Status Superseded Regulatory Stage Miscellaneous Binding Value Partial Legal effect AIACT. Regulation Visualiser Find more sources Related Long-form Insights on IndoPacific.App Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Related draft AI Law Provisions of aiact.in Section 21 – Intellectual Property Protections Section 21 – Intellectual Property Protections

AI as a Legal Entity The Indic Pacific Glossary The Complete Glossary AI as a Legal Entity Date of Addition 26 Apr 2024 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). Related Long-form Insights on IndoPacific.App Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 Learn More Legal Strategies for Open Source Artificial Intelligence Practices, IPLR-IG-004 Learn More Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More Previous Term Next Term 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

Generative AI applications with one standalone use case (GAI1) The Indic Pacific Glossary The Complete Glossary 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 Generative AI applications with one standalone use case (GAI1) Date of Addition 26 April 2024 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). Related Long-form Insights on IndoPacific.App Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Learn More Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 Learn More The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More [AIACT.IN V5] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 5 Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More Previous Term Next Term

AI Agents The Indic Pacific Glossary The Complete Glossary AI Agents Date of Addition 19 Jan 2025 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. Related Long-form Insights on IndoPacific.App The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More Previous Term Next Term 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

Liked our Work? Search it now on IndoPacific.App Get Searching Our Research Know more about our Knowledge Base, years of accumulated and developed in-house research at Indic Pacific Legal Research. Search our Research Treasure on IndoPacific.App. :) Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Get this Publication 2024 ISBN 978-81-977227-6-9 Author(s) Abhivardhan, Alisha Garg, Samyak Deshpande, Sanvi Zadoo Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-IG-010 Tags Abhivardhan, Accountability, AI, AI Ethics, AI Patentability, AI-assisted invention, AI-generated code, AI-generated content, AI-generated images, AI-generated invention, AI-generated music, AI-generated text, bias, context-specific AI governance, Copyright, copyright infringement, copyright transfer, creative commons, deepfakes, derivative work, disinformation, fair dealing, Fair Use, Generative AI, Governance, infringement threshold, Intellectual Property, inventorship, Licensing, misinformation, moral rights, non-obviousness, novelty, Open Source, original work, patent eligibility, patent examination, patent infringement, patent law, prior art, public disclosure, public domain, publishing, RBI FREE-AI Committee, regulation, statutory bars, substantial similarity, Transparency, utility Related Terms in Techindata.in Explainers Definitions - A - E AI Agents AI Anxiety AI Explainability Clause AI Knowledge Chain AI Literacy AI Supply Chain AI Value Chain Accountability Automation Definitions - F - J General intelligence applications with multiple short-run or unclear use cases as per industrial and regulatory standards (GI2) General intelligence applications with multiple stable use cases as per relevant industrial and regulatory standards (GI1) Generative AI applications with one standalone use case (GAI1) In-context Learning Indofuturism Intended Purpose / Specified Purpose Definitions - K - P Language Model Manifest Availability Model Algorithmic Ethics standards (MAES) Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Object-Oriented Design Proprietary Information Definitions - Q - U Roughdraft AI SOTP Classification Synthetic Content Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Definitions - V - Z Whole-of-Government Response Related Articles in Techindata.in Insights 34 Insight(s) on AI Ethics 9 Insight(s) on AI Governance 8 Insight(s) on AI and Competition Law 8 Insight(s) on AI and Copyright Law 8 Insight(s) on AI and media sciences 8 Insight(s) on AI regulation 8 Insight(s) on AI literacy 5 Insight(s) on AI and Evidence Law 4 Insight(s) on Abhivardhan 2 Insight(s) on AI and Intellectual Property Law 1 Insight(s) on AI and Securities Law 1 Insight(s) on Algorithmic Trading . Previous Item Next Item

Liked our Work? Search it now on IndoPacific.App Get Searching Our Research Know more about our Knowledge Base, years of accumulated and developed in-house research at Indic Pacific Legal Research. Search our Research Treasure on IndoPacific.App. :) Artificial Intelligence and Policy in India, Volume 6 [AIPI-V6] Get this Publication 2025 ISBN 978-81-977227-7-6 Author(s) Eva Mathur, Oshi Yadav, Rasleen Kaur Dua Editor(s) Abhivardhan IndoPacific.App Identifier (ID) AIPI-V6 Tags Abhivardhan, AI Ethics, Algorithmic Trading, Artificial Intelligence, Blockchain, digital economy, Distributed Ledger Technology, Financial Automation, Future of Legal Profession, India, indic pacific legal research, ISAIL, Law Students, Legal Education, Policy, Regulatory Challenges, Supply Chain Management, Technology Governance Related Terms in Techindata.in Explainers Definitions - A - E AI Literacy AI Supply Chain AI Value Chain Accountability Algorithmic Activities and Operations Automation CEI Classification Definitions - F - J Intended Purpose / Specified Purpose Definitions - K - P Language Model Manifest Availability Model Algorithmic Ethics standards (MAES) Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Object-Oriented Design Proprietary Information Definitions - Q - U Roughdraft AI SOTP Classification Synthetic Content Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 34 Insight(s) on AI Ethics 9 Insight(s) on AI Governance 8 Insight(s) on AI and Competition Law 8 Insight(s) on AI and Copyright Law 8 Insight(s) on AI and media sciences 8 Insight(s) on AI regulation 8 Insight(s) on AI literacy 5 Insight(s) on AI and Evidence Law 4 Insight(s) on Abhivardhan 2 Insight(s) on AI and Intellectual Property Law 1 Insight(s) on AI and Securities Law 1 Insight(s) on Algorithmic Trading . Previous Item Next Item