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AI as an Object The Indic Pacific Glossary The Complete Glossary AI as an Object Date of Addition 26 Apr 2024 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). Related Long-form Insights on IndoPacific.App 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 Regulative Methods for Generative AI [VLiGTA-TR-002] Learn More Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Learn More Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 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 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 [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Learn More AIACT.IN Version 3 Quick Explainer Learn More The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] 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 [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Learn More [AIACT.IN V5] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 5 Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] 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

Punjab and Haryana High Court March 2023 bail order marking first Indian judicial use of ChatGPT for researching bail jurisprudence in murder case. Jaswinder Singh @ Jassi v. State of Punjab & Anr., CRM-M-22496-2022, order dated 27-3-2023 Punjab and Haryana High Court March 2023 bail order marking first Indian judicial use of ChatGPT for researching bail jurisprudence in murder case. 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. March 2023 Read the Document Issuing Authority Punjab & Haryana High Court Type of Legal / Policy Document Judicial Pronouncements - National Court Precedents Status Enacted Regulatory Stage Miscellaneous Binding Value Legally binding instruments enforceable before courts 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 Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] 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 3 – Classification of Artificial Intelligence Section 3 – Classification of Artificial Intelligence Section 7 – Risk-centric Methods of Classification Section 7 – Risk-centric Methods of Classification Section 8 – Prohibition of Unintended Risk AI Systems Section 8 – Prohibition of Unintended Risk AI Systems

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 The LegalTechPolicy.com Playbook, First Edition Inspired by The LegalTechPolicy.com Playbook 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.

Released on December 8, 2025, by the Department for Promotion of Industry and Internal Trade (DPIIT), this consultation paper represents India's first institutional effort to reform copyright law for the artificial intelligence era. The document was prepared by an eight-member expert committee constituted on April 28, 2025, under the chairpersonship of Ms. Himani Pande (Additional Secretary, DPIIT), with Ms. Simrat Kaur (Director, Copyright, Design and CIPAM) serving as Member and Convenor. Working Paper on Generative AI and Copyright (Part 1): "One Nation One License One Payment" Released on December 8, 2025, by the Department for Promotion of Industry and Internal Trade (DPIIT), this consultation paper represents India's first institutional effort to reform copyright law for the artificial intelligence era. The document was prepared by an eight-member expert committee constituted on April 28, 2025, under the chairpersonship of Ms. Himani Pande (Additional Secretary, DPIIT), with Ms. Simrat Kaur (Director, Copyright, Design and CIPAM) serving as Member and Convenor. 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. December 2025 Read the Document Issuing Authority Department for Promotion of Industry and Internal Trade (DPIIT), Ministry of Commerce and Industry Type of Legal / Policy Document Guidance documents with normative influence Status Enacted Regulatory Stage Pre-regulatory Binding Value Guidance documents with normative influence AIACT. Regulation Visualiser Find more sources Related Long-form Insights on IndoPacific.App Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Learn More Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Learn More Related draft AI Law Provisions of aiact.in Section 10 – Composition and Functions of the Council Section 10 – Composition and Functions of the Council Section 15 – Guidance Principles for AI-related Agreements Section 15 – Guidance Principles for AI-related Agreements Section 16 – Guidance Principles for AI-related Corporate Governance Section 16 – Guidance Principles for AI-related Corporate Governance Section 17 – Post-Deployment Monitoring of High-Risk AI Systems Section 17 – Post-Deployment Monitoring of High-Risk AI Systems

WANA Date of Addition 13 March 2025 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. Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Global Customary International Law Index: A Prologue [GLA-TR-00X] Learn More Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Learn More India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] Learn More Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Learn More Global Legalism, Volume 1 Learn More Global Relations and Legal Policy, Volume 1 [GRLP1] Learn More South Asian Review of International Law, Volume 1 Learn More Indian International Law Series, Volume 1 Learn More Global Relations and Legal Policy, Volume 2 Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Learn More The Policy Purpose of a Multipolar Agenda for India, First Edition, 2023 Learn More Artificial Intelligence and Policy in India, Volume 4 [AIPI-V4] 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 AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Artificial Intelligence and Policy in India, Volume 6 [AIPI-V6] Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Next Term Previous Term 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

Issued on May 6, 2024, this was ECI's first formal advisory addressing deepfakes in election campaigning. The advisory directed political parties to take down deepfake posts within three hours of detection and established initial guidelines for responsible technology use during elections. This laid the foundation for subsequent more detailed advisories.​ Advisory on Ethical Use of Social Media and Deepfakes in Elections Issued on May 6, 2024, this was ECI's first formal advisory addressing deepfakes in election campaigning. The advisory directed political parties to take down deepfake posts within three hours of detection and established initial guidelines for responsible technology use during elections. This laid the foundation for subsequent more detailed advisories. 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. May 2024 Read the Document Issuing Authority Election Commission of India (ECI) Type of Legal / Policy Document Executive Instruments - Administrative Decisions Status In Force Regulatory Stage Regulatory Binding Value Non-binding but institutionally endorsed instruments AIACT. Regulation Visualiser Find more sources Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More Related draft AI Law Provisions of aiact.in Section 23 – Content Provenance and Identification Section 23 – Content Provenance and Identification

In-context Learning 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 In-context Learning Date of Addition 26 April 2024 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) . Related Long-form Insights on IndoPacific.App Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Learn More 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 NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More Previous Term Next Term

Delhi High Court September 2025 ex-parte order protecting actress against unauthorized AI chatbot impersonation and deepfake video exploitation. Aishwarya Rai Bachchan v. Aishwaryaworld.com & Ors., CS(COMM) 956/2025, Delhi High Court, Order dated September 9, 2025 Delhi High Court September 2025 ex-parte order protecting actress against unauthorized AI chatbot impersonation and deepfake video exploitation. 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. September 2025 Read the Document Issuing Authority Delhi High Court Type of Legal / Policy Document Judicial Pronouncements - National Court Precedents Status In Force Regulatory Stage Regulatory Binding Value Legally binding instruments enforceable before courts 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 Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] 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 Section 23 – Content Provenance and Identification Section 23 – Content Provenance and Identification

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, Market Power and India in a Multipolar World Get this Publication 2025 ISBN 978-81-990807-1-3 Author(s) Abhivardhan, Marius-Constantin Dinu, Sankalp Srivastava Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-SLDA-EXTAI-IG-001 Tags AI governance, AI value chain, AI washing, Alibaba Cloud, Amazon Web Services, Artificial Intelligence, Builder.ai, CARE Principles, cloud contracts, Community Data Trustees, data labelers, Data Sovereignty, economic future, economic law, electronic components, Google Cloud, inconsistent standards, India, Indigenous Data Governance, infrastructure, job losses, legal-policy intersections, localization, market intelligence, market power, Microsoft Azure, multilingual capabilities, multipolar world, National Critical Mineral Mission, open-source ethos, RBI FREE-AI Committee, regulatory arbitrage, restrictive contracts, semiconductor projects, state-led planning, strategic planning, tech law, vendor lock-in Related Terms in Techindata.in Explainers Definitions - A - E AI as an Industry AI as a Legal Entity AI as a Subject AI as a Third Party AI Doomerism AI Washing AI-based Anthropomorphization Accountability Agent Debt Anthropomorphism-based concept classification Derivative Generative AI Applications, the Generative AI products and services which are derivatives of the main generative AI applications, by virtue of reliance (DGAI) 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 Token Economics 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. :) AIACT.IN Version 3 Quick Explainer Get this Publication 2024 ISBN Not Applicable Author(s) Abhivardhan Editor(s) Not Applicable IndoPacific.App Identifier (ID) AIACT3E Tags Abhivardhan, AI applications, AI Development, AI Education, AI Ethics, AI Future, AI governance, AI Impact, AI Industry, AI Innovations, AI Research, AI Resources, AI Solutions, AI Technology, AI Tools, AI Training, AI Trends, AIACT.IN V3, Artificial Intelligence, Machine Learning Related Terms in Techindata.in Explainers Definitions - A - E AI as a Concept AI as an Object AI as a Subject AI as a Third Party Accountability Deepfakes Definitions - F - J Framework Fatigue 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 a collection of standalone use cases related to one another (GAI2) Intended Purpose / Specified Purpose Definitions - K - P Manifest Availability Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Parameters Privacy by Default Privacy by Design Proprietary Information Definitions - Q - U SOTP Classification Technology Transfer Transformer Model 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 regulation 8 Insight(s) on AI literacy 4 Insight(s) on Abhivardhan 4 Insight(s) on AIACT.in . Previous Item Next Item

Section 34 – Power to Remove Difficulties PUBLISHED Previous Next Section 34 - Power to Remove Difficulties (1) If any difficulty arises in giving effect to the provisions of this Act, the Central Government may, by order published in the Official Gazette, make such provisions, not inconsistent with the provisions of this Act as may appear to it to be necessary for removing the difficulty. (2) No such order shall be made under this Section after the expiry of a period of five years from the commencement of this Act. (3) Every order made under this Section shall be laid, as soon as may after it is made before each House of Parliament. Related Indian AI Regulation Sources

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. :) [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Get this Publication 2024 ISBN Not Applicable Author(s) Abhivardhan Editor(s) Not Applicable IndoPacific.App Identifier (ID) AIACT3 Tags Abhivardhan, AI applications, AI Development, AI Education, AI Ethics, AI Future, AI governance, AI Impact, AI Industry, AI Innovations, AI Research, AI Resources, AI Solutions, AI Technology, AI Tools, AI Training, AI Trends, AIACT.IN V3, Artificial Intelligence, Machine Learning Related Terms in Techindata.in Explainers Definitions - A - E AI as a Concept AI as an Object AI as a Subject AI as a Third Party Accountability Deepfakes Definitions - F - J Framework Fatigue 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 a collection of standalone use cases related to one another (GAI2) Intended Purpose / Specified Purpose Definitions - K - P Manifest Availability Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Parameters Privacy by Default Privacy by Design Proprietary Information Definitions - Q - U SOTP Classification Technology Transfer Transformer Model 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 regulation 8 Insight(s) on AI literacy 4 Insight(s) on Abhivardhan 4 Insight(s) on AIACT.in . Previous Item Next Item