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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. :) Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Get this Publication 2024 ISBN 978-81-972625-1-7 Author(s) Abhivardhan, Shirsha Ray Chaudhari Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-IG-005 Tags Abhivardhan, AI bias management, AI ethical considerations, AI governance, AI in Agriculture, AI in creative economy, AI in e-commerce, AI in Fintech, AI in healthcare, AI in manufacturing, AI integration, AI policy, AI recommendations, AI regulation, ai transparency, Digital Public Infrastructure, Ethical AI, Generative AI, IndiaStack, indic pacific legal research, Legal challenges AI, Sector-specific AI use, Shirsha Ray Chaudhari Related Terms in Techindata.in Explainers Definitions - A - E applicationgiri AI as a Component AI as a Concept AI as an Industry AI as a Juristic Entity AI as a Legal Entity AI as an Object AI as a Subject AI Knowledge Chain AI Literacy AI Supply Chain AI Value Chain AI Workflows AI-based Anthropomorphization Accountability App Crappers Artificial Intelligence Hype Cycle Automation 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 Small Language Models Synthetic Content Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Definitions - V - Z Whole-of-Government Response Related Articles in Techindata.in Insights 34 Insight(s) on AI Ethics 9 Insight(s) on AI Governance 8 Insight(s) on AI and Competition Law 8 Insight(s) on AI and Copyright Law 8 Insight(s) on AI and media sciences 8 Insight(s) on AI regulation 8 Insight(s) on AI literacy 5 Insight(s) on AI and Evidence Law 4 Insight(s) on Abhivardhan 2 Insight(s) on AI and Intellectual Property Law 1 Insight(s) on AI and Securities Law 1 Insight(s) on Algorithmic Trading . Previous Item Next Item

Released on December 29, 2025, by the Office of the Principal Scientific Adviser to the Government of India (Prof. Ajay Kumar Sood), this is the first white paper in the series on "Emerging Policy Priorities for India's AI Ecosystem". The white paper defines democratising access to AI infrastructure as making foundational AI resources—compute capacity, high-quality datasets, and enabling tools—available beyond a limited set of large firms and major urban hubs, treating them as shared national resources. Democratising Access to AI Infrastructure (White Paper, Version 3.0) Released on December 29, 2025, by the Office of the Principal Scientific Adviser to the Government of India (Prof. Ajay Kumar Sood), this is the first white paper in the series on "Emerging Policy Priorities for India's AI Ecosystem". The white paper defines democratising access to AI infrastructure as making foundational AI resources—compute capacity, high-quality datasets, and enabling tools—available beyond a limited set of large firms and major urban hubs, treating them as shared national resources. 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 Office of Principal Scientific Adviser (OPSA), Government of India 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 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 NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 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 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

Information Cosplay 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 Information Cosplay Date of Addition 4 November 2025 Information Cosplay refers to the superficial mimicry of authoritative information through AI-generated content that lacks genuine cognitive understanding, contextual awareness, or factual grounding. This phenomenon occurs when large language models and generative AI systems produce outputs that appear credible and informative while fundamentally lacking the identity, continuity, and epistemological rigour characteristic of authentic knowledge production. Information cosplay describes content that dresses itself in the formal appearance of legitimate information—using technical terminology, authoritative tone, and structured formatting—without possessing the underlying intellectual infrastructure that defines genuine knowledge work. Much like traditional cosplay involves wearing costumes to represent fictional characters, information cosplay involves AI systems "wearing" the surface markers of authoritative discourse without embodying the cognitive processes that generate genuine expertise. The phenomenon arises from fundamental technical limitations in current AI architectures. LLMs remain "frozen after training" with no genuine identity or continuity of thought. Fine-tuning does not alter the cognitive topology or manifold of these systems. They operate under insurmountable constraints imposed by information theory and Kullback-Leibler divergence, producing outputs that disguise their inherent limitations in data processing, algorithmic logic, and model validation. Information cosplay contributes to what can be termed "the age of slop" or "slopification"—a period characterized by mass production of content that imitates knowledge without embodying it. This represents a systemic degradation of information quality, contradicting optimistic narratives about entering an "Age of Intelligence." Rather than witnessing the emergence of genuine machine understanding, we observe the proliferation of increasingly sophisticated imitation. Information cosplay is not merely poor content or technical failure. It represents the inevitable byproduct of AI systems operating beyond their technical and epistemological boundaries, producing outputs that obscure rather than illuminate the actual capabilities and limitations of contemporary artificial intelligence systems. This conceptualisation of information cosplay draws upon critical insights from Denis O. (Fintech Professional, AI/ML Solution Architect) and Bogdan Grigorescu , whose observations on AI's fundamental technical limitations and the phenomenon of "slopification" provide essential correctives to misleading media narratives about artificial intelligence. Related Long-form Insights on IndoPacific.App Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 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 Previous Term Next Term

MeitY's October 2025 draft amendments mandating labeling of AI-generated content (10% screen area/duration), permanent metadata embedding, and enhanced due diligence for intermediaries on deepfakes and synthetically generated information. Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 2025 - Draft Rules on Synthetic and AI-Generated Content MeitY's October 2025 draft amendments mandating labeling of AI-generated content (10% screen area/duration), permanent metadata embedding, and enhanced due diligence for intermediaries on deepfakes and synthetically generated information. 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. October 2025 Read the Document Issuing Authority Ministry of Electronics and Information Technology (MeitY) Type of Legal / Policy Document Secondary Legislation Status Draft Regulatory Stage Pre-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 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

Distributed Ledger The Indic Pacific Glossary The Complete Glossary Distributed Ledger Date of Addition 26 Apr 2024 A distributed ledger (also called a shared ledger or distributed ledger technology or DLT) is the consensus of replicated, shared, and synchronized digital data that is geographically spread (distributed) across many sites, countries, or institutions. Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] 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 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

AI as an Industry The Indic Pacific Glossary The Complete Glossary AI as an Industry Date of Addition 26 Apr 2024 It means Artificial Intelligence may be considered as a sector or industry or industry segment (howsoever it is termed) in terms of its economic and social utility. This idea was proposed in the 2020 Handbook on AI and International Law (2021): As an industry, the economic and social utility of AI has to be in consensus with the three factors: (1) state consequentialism or state interests; (2) industrial motives and interests; and (3) the explanability and reasonability behind the industrial products and services central or related to AI. Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition 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 Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] 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 NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More Previous Term Next Term terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

Hierarchical Feedback Distortion 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 Hierarchical Feedback Distortion Date of Addition 5 March 2025 The Hierarchical Feedback Distortion Principle operates through a specific mechanism wherein state and central governments respond dramatically to negative feedback, often through public statements, high-profile investigations, or policy announcements. These responses, while highly visible, frequently fail to address the underlying structural issues that enable corruption or administrative failures at the local level. The resulting dynamic creates what can be described as "accountability gaps" – spaces within the governance system where certain actors can operate with relative impunity despite the appearance of oversight. These accountability gaps form through several interconnected processes. First, the distance between higher levels of government and local administration creates information asymmetries, where central authorities lack detailed knowledge of ground-level operations. Second, the emphasis on negative feedback creates incentives for performative responses that satisfy public demand for action without necessarily changing administrative practices. Third, the hierarchical nature of bureaucratic systems often shields lower-level officials from direct accountability to citizens, instead making them primarily answerable to superiors within the bureaucracy. In the Indian context, these dynamics are particularly pronounced due to the country's complex multi-level governance structure, which includes central, state, district, and local administrative tiers. Each level operates with different incentives, capacities, and relationships to citizens, creating multiple opportunities for accountability mechanisms to break down. The resulting system can inadvertently create protected spaces where corruption can flourish despite the appearance of active governance and oversight from above. This principle was created as a matter of inspiration of some of the posts by Pseudokanada, i.e., @hestmatematik on X . Related Long-form Insights on IndoPacific.App Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Previous Term Next Term

Telecommunication Engineering Centre's July 2023 voluntary standard providing procedures for self-certification and independent certification of AI system fairness. Fairness Assessment and Rating of Artificial Intelligence Systems (TEC 57050:2023) Telecommunication Engineering Centre's July 2023 voluntary standard providing procedures for self-certification and independent certification of AI system fairness. 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. July 2023 Read the Document Issuing Authority Telecom Regulatory Authority of India (TRAI) Type of Legal / Policy Document Secondary Legislation 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 11 – Registration & Certification of AI Systems Section 11 – Registration & Certification of AI Systems Section 13 – National Artificial Intelligence Ethics Code Section 13 – National Artificial Intelligence Ethics Code Section 22 – Shared Sector-Neutral & Sector-Specific Standards Section 22 – Shared Sector-Neutral & Sector-Specific Standards

SEBI's June 2025 principles-based AI/ML governance framework establishing India's first capital markets AI regulation requiring board-approved AI Governance Frameworks for market infrastructure institutions, intermediaries, and mutual funds; mandates six-pillar compliance (ethics, accountability, transparency, auditability, data privacy, fairness), explainability requirements, human oversight for critical market functions, and extensive documentation for supervisory review; consultation period closed July 11, 2025. Guidelines on Responsible Use of Artificial Intelligence and Machine Learning in Securities Markets SEBI's June 2025 principles-based AI/ML governance framework establishing India's first capital markets AI regulation requiring board-approved AI Governance Frameworks for market infrastructure institutions, intermediaries, and mutual funds; mandates six-pillar compliance (ethics, accountability, transparency, auditability, data privacy, fairness), explainability requirements, human oversight for critical market functions, and extensive documentation for supervisory review; consultation period closed July 11, 2025. 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. June 2025 Read the Document Issuing Authority Securities and Exchange Board of India (SEBI) 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 Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Learn More Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Learn More NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More Related draft AI Law Provisions of aiact.in Section 16 – Guidance Principles for AI-related Corporate Governance Section 16 – Guidance Principles for AI-related Corporate Governance Section 20-A – Transparency and Accountability in AI-related Government Initiatives and Public-Private Partnerships Section 20-A – Transparency and Accountability in AI-related Government Initiatives and Public-Private Partnerships Section 21-A – Data Classification and Localisation Requirements Section 21-A – Data Classification and Localisation Requirements

Notified by MeitY on November 13, 2025, these rules operationalize the Digital Personal Data Protection Act, 2023, establishing a comprehensive and enforceable framework for digital personal data protection in India. The rules were finalized after extensive public consultation on a draft released in January 2025, with MeitY receiving over 4,000 comments and conducting multi-city consultations to incorporate feedback from industry, civil society, and government departments.​ Digital Personal Data Protection Rules, 2025 Notified by MeitY on November 13, 2025, these rules operationalize the Digital Personal Data Protection Act, 2023, establishing a comprehensive and enforceable framework for digital personal data protection in India. The rules were finalized after extensive public consultation on a draft released in January 2025, with MeitY receiving over 4,000 comments and conducting multi-city consultations to incorporate feedback from industry, civil society, and government departments. Previous Next The AIACT.IN India AI Regulation Tracker This is a simple regulatory tracker consisting all information on how India is regulating artificial intelligence as a technology, inspired from a seminal paper authored by Abhivardhan and Deepanshu Singh for the Forum of Federations, Canada, entitled, "Government with Algorithms: Managing AI in India’s Federal System – Number 70 ". We have also included case laws along with regulatory / governance documents, and avoided adding any industry documents or policy papers which do not reflect any direct or implicit legal impact. November 2025 Read the Document Issuing Authority Ministry of Electronics and Information Technology (MeitY) Type of Legal / Policy Document Secondary Legislation Status Enacted Regulatory Stage Regulatory Binding Value Secondary Legislation 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 NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 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 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

Section 4 – Conceptual Methods of Classification PUBLISHED Previous Next Section 4 – Conceptual Methods of Classification (1) These methods as designated in clause (i) of sub-section (1) of Section 3 categorize artificial intelligence technologies through a conceptual assessment of their utilisation, development, maintenance, and proliferation to examine & recognise their inherent purpose. This classification is further categorised as – (i) Issue-to-Issue Concept Classification (IICC) as described in sub-section (2) (ii) Ethics-Based Concept Classification (EBCC) as described in in sub-section (3) (iii) Phenomena-Based Concept Classification (PBCC) as described in in sub-section (4) (iv) Anthropomorphism-Based Concept Classification (ABCC) as described in in sub-section (5) (2) Issue-to-Issue Concept Classification (IICC) involves the method to determine the inherent purpose of artificial intelligence technologies on a case-to-case basis, to examine & recognise their inherent purpose on the basis of these factors of assessment: (i) Utilisation: Assessing the specific use cases and applications of the AI technology in various domains. (ii) Development: Evaluating the design, training, and deployment processes of the AI technology. (iii) Maintenance: Examining the ongoing support, updates, and modifications made to the AI technology. (iv) Proliferation: Analysing the dissemination and adoption of the AI technology across different sectors and user groups. Illustrations (1) An AI system designed for medical diagnostics is classified based on its purpose to enhance patient outcomes. For instance, if an AI software assists doctors in diagnosing diseases more accurately, it is classified under medical AI applications. (2) An AI system for financial trading is classified based on its purpose to optimize investment strategies. For example, if an AI-driven algorithm analyses market data to recommend stock trades, it is classified under financial AI applications. (3) Ethics-Based Concept Classification (EBCC) involves the method of recognising the ethics-based relationship of artificial intelligence technologies in sector-specific & sector-neutral contexts, to examine & recognise their inherent purpose on the basis of these factors: (i) Utilisation: Evaluating how AI technology impacts ethical principles during its use in specific sectors or across multiple domains. (ii) Development: Assessing whether ethical considerations were integrated during the design, training, and deployment phases of the AI technology. (iii) Maintenance: Examining how ethical responsibilities are upheld during updates and modifications to the AI system. (iv) Proliferation: Analyzing how the widespread adoption of the AI system affects ethical standards across sectors and user groups. Illustration An AI for social media content moderation is assessed based on fairness and bias prevention. For example, if an AI filters hate speech and misinformation on social media platforms, it is classified under content moderation AI with an emphasis on ensuring unbiased and fair treatment of all users’ content. (4) Phenomena-Based Concept Classification (PBCC) involves the method of addressing rights-based issues associated with the use and dissemination of artificial intelligence technologies to examine & recognise their inherent purpose on the basis of these factors: (i) Utilisation: Assessing how the AI system affects individual or collective rights during its use in various domains. (ii) Development: Evaluating whether evaluates whether AI systems incorporate protections for rights recognized under Indian law during their design, training, and deployment phases, considering legal constitutional, and commercial rights. (iii) Maintenance: Reviewing how ongoing support and updates to the AI system protect user rights. (iv) Proliferation: Analysing the rights-based implications of AI technology dissemination and adoption across different sectors and user groups. Illustrations (1) An AI system that analyses personal data for targeted advertising is classified based on its compliance with data protection rights. For example, an AI that personalizes ads based on user behaviour is classified under advertising AI with data privacy considerations. (2) An AI used in autonomous vehicles is classified based on its implications for road safety and user rights. For instance, an AI that controls self-driving cars is classified under automotive AI with a focus on safety and user rights. (5) Anthropomorphism-Based Concept Classification (ABCC) involves the method of evaluating scenarios where AI systems ordinarily simulate, imitate, replicate, or emulate human attributes, which include: (i) Autonomy: The ability to operate and make decisions independently, based on a set of corresponding scenarios including but not limited to: • Simulation: AI systems model autonomous decision-making processes using computational methods; • Imitation: AI systems learn from and reproduce human-like autonomous behaviours; • Replication: AI systems accurately reproduce specific human-like autonomous functions; • Emulation: AI systems replicate and potentially enhance human-like autonomy; Illustration An AI-powered drone delivery system that navigates through urban environments, avoiding obstacles and adapting its route based on real-time traffic conditions to efficiently deliver packages without human intervention. (ii) Perception: The ability to interpret and understand sensory information from the environment, based on a set of corresponding scenarios including but not limited to: • Simulation: AI systems model human-like perception using computational methods; • Imitation: AI systems learn from and reproduce specific human-like perceptual processes; • Replication: AI systems accurately reproduce specific human-like perceptual abilities; Illustration A service robot in a hotel uses computer vision and natural language processing to recognize and greet guests by name, interpret their facial expressions and tone of voice to gauge emotions, and respond appropriately to verbal requests. (iii) Reasoning: The ability to process information, draw conclusions, and solve problems, based on a set of corresponding scenarios including but not limited to: • Simulation: AI systems model human-like reasoning using computational methods; • Imitation: AI systems learn from and reproduce specific human reasoning patterns; • Replication: AI systems accurately reproduce specific human-like reasoning abilities; • Emulation: AI systems surpass specific human-like reasoning abilities; Illustration A medical diagnosis AI system analyses a patient’s symptoms, medical history, test results and imaging scans. It uses this information to generate a list of probable diagnoses, suggest additional tests to rule out possibilities, and recommend an optimal treatment plan. (iv) Interaction: The ability to communicate and engage with humans or other AI systems, based on a set of corresponding scenarios including but not limited to: • Simulation: AI systems model human-like interaction using computational methods; • Imitation: AI systems learn from and reproduce specific human interaction patterns; • Replication: AI systems accurately reproduce specific human-like interaction abilities; • Emulation: AI systems enhance human-like interaction; Illustration An AI-powered virtual assistant engages in natural conversations with users, understanding context and nuance. It asks clarifying questions when needed, provides relevant information or executes tasks, and even interjects with suggestions or prompts. (v) Adaptation: The ability to learn from experiences and adjust behaviour accordingly, based on a set of corresponding scenarios including but not limited to: • Simulation: AI systems model human-like adaptation using computational methods. • Imitation: AI systems learn from and reproduce human adaptation behaviours. • Replication: AI systems reproduce human-like adaptation abilities, recognizing the inherent complexity. • Emulation: AI systems surpass human-like adaptation as an aspirational goal. Illustration An AI system for stock trading continuously analyses market trends, world events, and the performance of its own trades. It identifies patterns and correlations, learning which strategies work best in different scenarios. The AI optimizes its trading algorithms and adapts its approach based on accumulated experience, demonstrating adaptive abilities. (vi) Creativity: The ability to generate novel ideas, solutions, or outputs, based on a set of corresponding scenarios including but not limited to: • Simulation: AI systems model human-like creativity using computational methods; • Imitation: AI systems learn from and reproduce human creative processes; • Replication: AI systems accurately reproduce human-like creative abilities, acknowledging the complexity involved; • Emulation: AI systems enhance human-like creativity as a forward-looking objective; Illustration An AI music composition tool creates an original symphony. Given a theme and emotional tone, it generates unique melodies, harmonies and instrumentation. It iterates and refines the composition based on aesthetic evaluation models, ultimately producing a piece that is distinct from existing music in its training data. Related Indian AI Regulation Sources

Section 33 – Savings Clause PUBLISHED Previous Next Section 33 - Savings Clause (1) The provisions of this Act shall be in addition to, and not in derogation of, the provisions of any other law for the time being in force. (2) Nothing in this Act shall affect the validity of any action taken or decision made by any entity in relation to the development, deployment, or use of AI systems prior to the commencement of this Act, provided such action or decision was in accordance with the laws in force at that time. (3) Any investigation, legal proceeding, or remedy in respect of any right, privilege, obligation, liability, penalty, or punishment under any law, initiated or arising before the commencement of this Act, shall be continued, enforced, or imposed as if this Act had not been enacted. (4) Nothing in this Act shall be construed as preventing the Central Government from making any rules or regulations, or taking any action, which it considers necessary for the purpose of removing any difficulty that may arise in giving effect to the provisions of this Act. Related Indian AI Regulation Sources