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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 Advisory on Enhanced Standards for AI-Generated and Synthetic Content in Elections (Bihar Assembly Elections) October 2025 Aishwarya Rai Bachchan v. Aishwaryaworld.com & Ors., CS(COMM) 956/2025, Delhi High Court, Order dated September 9, 2025 September 2025 Akkineni Nagarjuna v. www.bfxxx.org & Ors., CS(COMM) 1023/2025, Delhi High Court, Order dated September 25, 2025 September 2025 Sudhir Chaudhary v. Meta Platforms Inc & Ors., CS(COMM) 1089/2025, Delhi High Court, Order dated October 10, 2025 October 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 Hrithik Roshan v. Ashok Kumar/John Doe & Ors., CS(COMM) 1107/2025, Delhi High Court, Order dated October 15, 2025 October 2025

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. :) 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Get this Publication 2021 ISBN 978-81-957087-1-0 Author(s) Abhivardhan, Aditi Sharma, Akash Manwani, Arundhati Kale, Dev Tejnani, Manohar Samal, Mayank Narang, Mridutpal Bhattacharyya, Saakshi Agarwal, Sameer Samal, Sanad Arora Editor(s) Abhivardhan, Akash Manwani, Kshitij Naik, Suman Kalani IndoPacific.App Identifier (ID) RHB 2020 ISAIL Tags AI, Data Science, Ethics, Governance, Handbook, Innovation, International Law, Legal Studies, Policy 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 AI Explainability Clause Accountability 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 Multipolar World Multipolarity Omnipotence Omnipresence Phenomena-based concept classification Privacy by Default Privacy by Design Definitions - Q - U SOTP Classification Semi-Supervised Learning Strategic Autonomy Technical concept classifcation Technology by Default Technology Distancing Technology Transfer Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 29 Insight(s) on AI Ethics 8 Insight(s) on AI and Copyright Law 7 Insight(s) on AI and Competition Law 7 Insight(s) on AI and media sciences 7 Insight(s) on AI regulation 5 Insight(s) on AI Governance 3 Insight(s) on AI and Evidence Law 3 Insight(s) on AI literacy 2 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

Generative AI applications with a collection of standalone use cases related to one another (GAI2) Explainers 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 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

Section 10-A – Composition and Functions of the Institute PUBLISHED Previous Next Section 10-A – Composition and Functions of the Institute (1) With effect from the date notified by the Central Government, there shall be established the Indian Artificial Intelligence Safety Institute (AISI), a statutory body for the purposes of this Act. (2) The Indian Artificial Intelligence Safety Institute (AISI) shall be established as an autonomous body corporate with perpetual succession, a common seal, and the power to acquire, hold and transfer property, both movable and immovable, and to contract and be contracted, and sue or be sued by its name. (3) The Governing Body of the Indian Artificial Intelligence Safety Institute shall consist of the following members: (i) A Director General of AI Safety, with at least 15 years of experience in artificial intelligence research, who shall serve as the Chief Executive Officer of AISI. (ii) One representative from the Ministry of Electronics and Information Technology (MeitY), not below the rank of Joint Secretary. (iii) One representative from the Ministry of Science and Technology (DST), not below the rank of Joint Secretary. (iv) One representative from the Ministry of Defence, not below the rank of Joint Secretary. (v) One representative from the Ministry of Communications, not below the rank of Joint Secretary. (vi) One representative from NITI Aayog, not below the rank of Joint Secretary. (vii)One representative from the Committee for AI Centers of Excellence (CoEs) as an ex-officio member (viii) One Representative from the Committee for Technical Institutions in Critical AI Research as an ex-officio member (ix) One Representative from the Committee on AI Ethics and Safety as an ex-officio member (4) In addition to the Governing Body, AISI shall include the following ex-officio members: (i) The Principal Scientific Advisor to the Government of India, or their nominee. (ii) One member from the Prime Minister’s Economic Advisory Council . (iii) One representative, being a government official or expert appointed by the Central Government, responsible for coordinating with global AI safety institutes to ensure knowledge exchange and collaboration on emerging risks and best practices. (5) The AISI shall establish specialized committees as deemed necessary for fulfilling its mandate. These committees shall include but are not limited to: (i) Committee for AI Centers of Excellence (CoEs) : This committee shall represent all AI-related Centers of Excellence across India. (ii) Committee for Technical Institutions in Critical AI Research : This committee shall coordinate with technical institutions engaged in critical research on AI systems. (iii) Committee on AI Ethics and Safety : This committee shall guide AISI on ethical principles governing AI systems. (6) The AISI shall undertake the following functions under this Act: (i) Develop protocols for risk assessment, monitoring, and mitigation concerning high-risk AI applications, particularly in strategic sectors such as healthcare, defence, finance, and public administration. (ii) Formulate and establish safety standards for high-risk AI applications for the IAIC. These standards shall be aligned with national security priorities and international norms governing AI safety. (iii) Conduct annual audits of high-risk AI systems deployed across various sectors. The findings from these audits shall be reported to IAIC for further action or policy formulation. (iv) Undertake research initiatives focused on identifying emerging risks associated with new developments in artificial intelligence. Such research shall be conducted in partnership with IAIC, academic institutions, technical bodies and centres of excellence (CoEs), and international organizations dedicated to AI safety. (v) Submit an annual report to the Central Government and IAIC, detailing safety incidents, audit findings, and research advancements. (7) AISI may engage in international partnerships and dialogues, contributing to India’s leadership in responsible AI governance. 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. :) Global Relations and Legal Policy, Volume 2 Get this Publication 2021 ISBN 978-81-947926-4-2 Author(s) Adrija Ghosh, Anirudh Vats, Beghuman Simsir, Chitrika Grover, Hriti Parekh, Ishita Thakur, Mahak Gupta, Manohar Samal, Nikita Mulay, Sameep Khanal, Sanchana Srivastava, Srishti Pareek, Subodh Singh Editor(s) Abhivardhan IndoPacific.App Identifier (ID) GRLP2 Tags International Law, Public Policy Related Terms in Techindata.in Explainers Definitions - A - E CEI Classification Class-of-Applications-by-Class-of-Application (CbC) approach Definitions - F - J GAE Indo-Pacific International Algorithmic Law Definitions - K - P Multi-alignment Multipolar World Multipolarity Permeable Indigeneity in Policy (PIP) Phenomena-based concept classification Definitions - Q - U Strategic Autonomy Strategic Hedging Technophobia Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 4 Insight(s) on Government Affairs 1 Insight(s) on India-US Relations 1 Insight(s) on governance 1 Insight(s) on Indic Pacific 1 Insight(s) on India 1 Insight(s) on strategic sectors . Previous Item Next Item

Issued by the Department of Expenditure in February 2025, this communication determined that AI tools and AI apps in office computers and devices pose risks to the confidentiality of government data and documents. The advisory strictly prohibits employees from using any AI tools or apps in office devices. This represents a complete ban approach similar to ESIC's policy but applied across the Finance Ministry.​ India AI Regulation Landscape 101 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. Advisory on Prohibition of AI Tools/Apps in Office Devices Issued by the Department of Expenditure in February 2025, this communication determined that AI tools and AI apps in office computers and devices pose risks to the confidentiality of government data and documents. The advisory strictly prohibits employees from using any AI tools or apps in office devices. This represents a complete ban approach similar to ESIC's policy but applied across the Finance Ministry. Previous Next February 2025 Issuing Authority Ministry of Finance, Department of Expenditure Type of Legal / Policy Document Executive Instruments - Administrative Decisions Status In Force Regulatory Stage Regulatory Binding Value Legally binding instruments enforceable before courts Read the Document AI Regulation Visualisation Related Long-form Insights on IndoPacific.App 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 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

Federated Learning Explainers 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 Federated Learning Date of Addition 22 March 2025 A decentralised machine learning approach where multiple organizations or devices train models collaboratively without sharing raw data. Instead, only model updates or parameters are exchanged, ensuring data privacy while leveraging distributed data for improved model accuracy. Federated learning involves a two-step process: during training, local models are trained on local datasets with only parameters (not raw data) exchanged between participants to build a global model; during inference, the model is stored on the user device for quick predictions. This approach offers several advantages: privacy-preserving AI development, personalised and adaptive models, lower bandwidth usage, and improved security through decentralisation. Related Long-form Insights on IndoPacific.App Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Learn More Previous Term Next Term

Retrieval-Augmented Generation (RAG) Date of Addition 17 October 2025 A hybrid AI framework that combines large language models with external information retrieval systems to generate responses grounded in authoritative, real-time data sources rather than relying solely on static training data. RAG operates by fetching relevant documents from databases, knowledge bases, or repositories before the LLM generates output, thereby reducing hallucinations, improving factual accuracy, and enabling domain-specific responses without costly model retraining. The technique addresses fundamental LLM limitations including outdated information, terminology confusion, and inability to access proprietary organizational knowledge. Related Long-form Insights on IndoPacific.App Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Learn More NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More Previous Term Next Term Explainers 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

Privacy by Design Date of Addition 26 April 2024 Privacy by Design states that any action a company undertakes that involves processing personal data must be done with data protection and privacy in mind at every step. This was largely proposed in the Article 25 of the General Data Protection Regulation of the European Union. Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] 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 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 Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 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 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Previous Term Next Term Explainers 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

Technophobia Date of Addition 19 January 2025 An irrational or disproportionate fear, aversion, or resistance to advanced technologies, technological change, and digital innovation. Manifests as psychological and physiological responses ranging from mild anxiety to severe distress when interacting with or contemplating technological systems. Often characterised by: Cognitive resistance to learning new technological skills Physical symptoms when forced to use technology Avoidance behaviours toward digital tools and platforms Distinguished from rational technology criticism by its emotional rather than analytical basis. Particularly relevant in contexts of rapid technological transformation, AI adoption, and digital transformation initiatives. Related Long-form Insights on IndoPacific.App 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 Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Learn More Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Learn More Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 Learn More The Policy Purpose of a Multipolar Agenda for India, First Edition, 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 Artificial Intelligence and Policy in India, Volume 4 [AIPI-V4] Learn More Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Learn More The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Learn More Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] Learn More Indic Pacific - ISAIL Joint Annual Report, 2022-24 Learn More The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 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 The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] 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 Artificial Intelligence, Market Power and India in a Multipolar World Learn More Previous Term Next Term Explainers 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

Section 14 – Model Standards on Knowledge Management PUBLISHED Previous Next Section 14 - Model Standards on Knowledge Management (1) The IAIC shall develop, document and promote comprehensive model standards on knowledge management practices concerning the development, maintenance, and governance of high-risk AI systems. These standards shall focus on the effective management of knowledge assets; (2) The model standards shall encompass the following areas: (i) Intellectual property management practices to safeguard and leverage AI-related intellectual property rights such as patents, copyrights, trademarks and industrial designs. (ii) Processes for documenting and organizing technical knowledge assets like research reports, manuals, standards and industrial practices related to AI systems. (iii) Frameworks for capturing, retaining and transferring the tacit knowledge and expertise of human capital involved in AI development and deployment. (iv) Organisational systems and methodologies to enable effective knowledge capture, storage, retrieval and utilisation across the AI system lifecycle. (v) Mechanisms for leveraging customer-related knowledge assets such as data, feedback and insights to enhance AI system development and performance. (vi) Analytical techniques to derive knowledge from data analysis, including identifying patterns, trends and developing predictive models for AI systems. (vii)Collaborative practices to foster cross-functional knowledge sharing and generation through teams, communities of practice and other initiatives. (3) All entities engaged in the development, deployment, or utilisation of high-risk AI systems shall be bound by the model standards on knowledge management and decision-making as provided by this section. The compliance timeline for such high-risk AI systems shall be determined by the IAIC and may vary based on the technical, commercial and risk-based classification of those systems under Section 12. (4) For artificial intelligence technologies subject to commercial classification as determined by the factors outlined in sub-section (1) of Section 6, the requirement to comply with these model standards on knowledge management shall be assessed by the IAIC on a case-by-case basis, taking into consideration the specific commercial classification factors applicable to each AI technology. Illustration A startup has developed an AI-powered language translation app that allows users to translate text, documents, and speech between multiple Indian languages. Based on an assessment of the factors in Section 6(1), such as the app’s user base, market influence, and data integration, the IAIC may determine that this AI technology falls under the AI-Pro or AIaaS category. The IAIC will then evaluate if the startup needs to fully comply with the knowledge management standards or if certain requirements can be relaxed or made optional based on the app’s specific use case and commercial profile. (5) In determining the case-by-case application of these model standards to commercially classified AI technologies under sub-section (1) of Section 6, the IAIC shall take into account any relevant sector-specific standards, codes of practice, or regulatory guidelines pertaining to knowledge management practices in the sector to which the AI technology belongs or is intended to be deployed. Illustration An agritech startup has developed an AI system that analyzes satellite imagery and weather data to provide crop yield predictions and advisory services to farmers. As this AI technology falls within the agriculture sector, the IAIC’s assessment of its knowledge management requirements will consider any relevant guidelines or standards issued by bodies like the Indian Council of Agricultural Research (ICAR) or the Ministry of Agriculture & Farmers’ Welfare. These may include data governance norms for agricultural data, model validation protocols for AI-based advisory services, or best practices for maintaining data trails and audit logs in agritech applications. (6) Failure to adhere to the prescribed model standards for knowledge management and decision-making processes shall result in regulatory actions by the IAIC, which may include: (i) Issuance of show-cause notices to the non-compliant entity, requiring them to explain the reasons for non-compliance and outline corrective measures within a specified timeline. (ii) Imposition of monetary penalties, determined based on the severity of non-compliance, the risk level of the AI system involved, and the potential impact on individuals, businesses, or society. The monetary penalties shall be commensurate with the financial capacity of the non-compliant entity. (iii)Suspension or revocation of certifications or registrations related to the non-compliant AI system, preventing its further development, deployment, or operation until compliance is achieved. (iv) Mandating independent audits of the non-compliant entity’s knowledge management and decision-making processes at their own cost, with the audit reports to be submitted to the IAIC for review and further action. (v) Issuing directives to the non-compliant entity to implement specific remedial measures, such as enhancing data quality controls, improving model governance frameworks, or strengthening decision-making procedures, within a defined timeline. (vi) In cases of persistent or egregious non-compliance, the IAIC may recommend the temporary or permanent suspension of the non-compliant entity’s AI-related operations, subject to due process and the principles of natural justice. (vii) Any other regulatory action deemed necessary and proportionate by the IAIC to ensure compliance with the prescribed model standards and to safeguard the responsible development, deployment, and use of high-risk AI systems. (7) The IAIC shall encourage the sharing of AI-related knowledge, including datasets, models, and algorithms, through open-source software repositories and platforms, subject to applicable intellectual property rights and the provisions of the Digital Personal Data Protection Act, 2023 and other relevant data protection and governance frameworks as may be prescribed. Related Indian AI Regulation Sources National Strategy for Artificial Intelligence (#AIforAll) June 2018

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 29 Insight(s) on AI Ethics 8 Insight(s) on AI and Copyright Law 7 Insight(s) on AI and Competition Law 7 Insight(s) on AI and media sciences 7 Insight(s) on AI regulation 5 Insight(s) on AI Governance 3 Insight(s) on AI and Evidence Law 3 Insight(s) on AI literacy 2 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