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Section 18 – Third-Party Vulnerability Reporting PUBLISHED Previous Next Section 18 - Third-Party Vulnerability Reporting [***] This is a repealed draft provision. Please click on "Next" to check the next draft provision / section. Related Indian AI Regulation Sources Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011 (SPDI Rules) April 2011 Reporting for Artificial Intelligence (AI) and Machine Learning (ML) applications and systems offered and used by market participants January 2019 Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules 2021) February 2021 Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 2023 (IT Amendment Rules 2023) April 2023 Digital Personal Data Protection Act, 2023 (DPDPA) August 2023 Draft Digital Personal Data Protection Rules, 2025 (DPDP Rules) January 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. :) Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Get this Publication 2023 ISBN 978-81-959932-0-8 Author(s) Abhivardhan, Kapil Naresh, Yashudev Bansal Editor(s) Not Applicable IndoPacific.App Identifier (ID) VLiGTA-TR-002 Tags Abhivardhan, AI Ethics, AI governance, AI regulation, Algorithmic accountability, Algorithmic governance, Artificial Intelligence, Data ethics, Emerging technologies, Ethical implications, Fairness and bias, Generative AI, Legal implications, Machine Learning, Policy and governance, Privacy and security, Regulative methods, Regulatory frameworks, Responsible AI, Societal impact of AI, Technology and society, VLiGTA 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 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 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 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 Retrieval-Augmented Generation (RAG) Roughdraft AI SOTP Classification Synthetic Content Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 34 Insight(s) on AI Ethics 9 Insight(s) on AI Governance 8 Insight(s) on AI and Competition Law 8 Insight(s) on AI and Copyright Law 8 Insight(s) on AI and media sciences 8 Insight(s) on AI regulation 8 Insight(s) on AI literacy 5 Insight(s) on AI and Evidence Law 4 Insight(s) on Abhivardhan 2 Insight(s) on AI and Intellectual Property Law 1 Insight(s) on AI and Securities Law 1 Insight(s) on Algorithmic Trading . Previous Item Next Item

Agent Debt The Indic Pacific Glossary The Complete Glossary Agent Debt Date of Addition 26 May 2026 Agent debt is the compounded cost of rapidly prototyping autonomous AI workflows without establishing rigorous architectural hygiene. It is a modern, AI-specific evolution of technical debt. When developers build agentic systems prioritizing speed over structure, they accumulate hidden complexities. Unlike traditional software where technical debt results in buggy but deterministic code, agent debt results in emergent, unpredictable, and degrading behaviour over time. Here is a breakdown of how agent debt accumulates and why it is so difficult to manage. Core Drivers of Agent Debt Prompt Collision: Adding "band-aid" instructions (e.g., "Never do X," "Always do Y") to fix immediate edge cases. Over time, these stacked system prompts conflict, causing the agent to become paralyzed or behave erratically because it cannot satisfy all constraints simultaneously. Memory Pollution: Failing to curate what the agent remembers. When raw logs, irrelevant conversational tangents, and outdated facts are dumped into the context window, the agent's reasoning degrades. It starts prioritizing noisy past data over current instructions. Tool Overlap: Providing an agent with multiple functions or APIs without clear, mutually exclusive descriptions. The agent wastes compute trying to figure out which tool to use, gets stuck in infinite loops, or executes the wrong action entirely. Context Entanglement: Building workflows where the output of one poorly defined agentic task feeds directly into another. If the first agent slightly alters its formatting or reasoning, the downstream agent silently fails or hallucinates. Why Agent Debt is More Dangerous Than Technical Debt Traditional technical debt is relatively straightforward to debug: a poorly written function will eventually throw an error, and a stack trace will point you to the exact line of code that broke. Agent debt is insidious because the system rarely crashes outright. Instead, it "fails silently" by producing slightly worse logic, taking longer to execute, or doing "weird things." Because the logic is probabilistic rather than deterministic, diagnosing the root cause—whether it was a polluted memory vector, a conflicting system prompt, or a misunderstood tool description—becomes a massive forensic undertaking. The "High-Interest Credit Card" Gary Marcus's reference to the 2014 Google paper, "Machine Learning: The High-Interest Credit Card of Technical Debt," perfectly contextualizes this. The paper argued that it is remarkably easy to build ML models fast, but massively expensive to maintain them because everything is entangled—changing one input changes the entire system's behaviour. Agent debt is the ultimate high-interest credit card. You get an impressive demo working in a weekend, but six months later, the system is a fragile house of cards, and the interest payments—the time spent debugging hallucinations and wrestling with prompt conflicts—bankrupt the engineering team's velocity. Related Long-form Insights on IndoPacific.App Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition 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

Jurisprudential Compression Tax 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 Jurisprudential Compression Tax Date of Addition 25 May 2026 The intellectual and predictive penalty incurred when the structural, linguistic, and historical complexities of Indian law are artificially simplified into sterile computational formats to accommodate the constraints of machine learning models. Key Characteristics In practice, this "tax" manifests as a degradation of legal fidelity through four primary mechanisms: Epistemic Downgrade: The delegation of complex semantic annotation—which typically requires seasoned legal expertise—to inexperienced actors (such as undergraduate law students) to achieve dataset scale. Binary Reductionism: The flattening of highly nuanced, multi-layered judicial outcomes (e.g., partial appellate modifications) into simplistic binary classifications (1 = accepted, 0 = rejected). Contextual Erasure: The deliberate stripping away of vital rhetorical frameworks—including statutes, precedents, and judicial arguments—in an attempt to isolate pure "facts," thereby blinding the model to the actual judicial calculus. Algorithmic Compromise: The reliance on technologically compressed (quantized) models due to regional or academic compute constraints, resulting in high hallucination rates and predictive capabilities that underperform legacy baseline models. Usage Context The term is primarily used to critique the rapid, uncritical digitization of the Indian legal ecosystem, warning that forcing "algorithmic legibility" onto a sprawling, multilingual judicial system automates intellectual laziness rather than advancing true legal artificial intelligence. Attribution This definition is derived from the essay " The Jurisprudential Compression Tax " by Prathik Karthikeyan, published on February 20, 2026, in the Substack publication UNFILTERED : Law, Tech, Startups . The references to specific research projects, datasets, models, or platforms (including TathyaNyaya , FactLegalLlama , and Substack ) are drawn directly from the source material for the purpose of accurate attribution and contextualizing the term's origin. Their inclusion in this glossary definition does not constitute an endorsement, affiliation, sponsorship, or independent verification of the original author's claims regarding these entities. Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] Learn More The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] 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 Previous Term Next Term

Accountability The Indic Pacific Glossary The Complete Glossary Accountability Date of Addition 22 Mar 2025 The responsibility of AI developers, organizations, and stakeholders to ensure AI systems operate ethically, legally, and transparently. It involves mechanisms that enable AI decision-making to be monitored, explained, and challenged when necessary. Accountability in AI can be categorised into several types: procedural accountability (ensuring transparent development processes), operational accountability (focusing on system performance and outcomes), ethical accountability (aligning AI with ethical norms), and legal accountability (complying with relevant regulations). In automated decision-making contexts, accountability ensures decisions are justified and transparent. Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Learn More An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] Learn More Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] 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 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 The Policy Purpose of a Multipolar Agenda for India, First Edition, 2023 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 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 [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 Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] 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 Sections 4-9, AiACT.IN V4 Infographic Explainers Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] 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 The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] 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 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

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. :) Draft Digital Competition Bill, 2024 for India: Feedback Report [IPLR-IG-003] Get this Publication 2024 ISBN 978-81-970837-0-9 Author(s) Abhivardhan, Krati Singh Bhadouriya, Shresh Kiran Narang, Vaishnavi Singh Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-IG-003 Tags 2024, Abhivardhan, ai accountability, Antitrust Legislation, Competition law, competition policy, Consumer Protection, digital economy, Digital Platforms, Draft Digital Competition Bill, Economic Regulation, feedback report, Government Legislation, India, Industry Feedback, Legal Reform, Market Dynamics, Market Regulation, Policy Development, Regulatory Framework, Technology Sector Related Terms in Techindata.in Explainers Definitions - A - E AI Supply Chain AI Value Chain Compute Definitions - F - J Framework Fatigue Indo-Pacific Definitions - K - P Object-Oriented Design Omnipotence Omnipresence Polyvocality Performance Effect Phenomena-based concept classification 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 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 July 19, 2025, by the Kerala High Court, this is India's first formally documented and binding set of guidelines restricting AI use in district and subordinate courts. The policy is addressed to all District Judges and Chief Judicial Magistrates with directions to communicate to all judicial officers and staff members under their jurisdiction. Policy Regarding Use of Artificial Intelligence Tools in District Judiciary Released on July 19, 2025, by the Kerala High Court, this is India's first formally documented and binding set of guidelines restricting AI use in district and subordinate courts. The policy is addressed to all District Judges and Chief Judicial Magistrates with directions to communicate to all judicial officers and staff members under their jurisdiction. 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 2025 Read the Document Issuing Authority Kerala High Court Type of Legal / Policy Document Executive Instruments - Administrative Decisions 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 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 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

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. :) Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Get this Publication 2025 ISBN 978-81-986924-1-2 Author(s) Abhivardhan Editor(s) Not Applicable IndoPacific.App Identifier (ID) Norm_Geo_IAL Tags Abhivardhan, AI adaptability, AI Ethics, AI frameworks, AI governance, AI Hype, AI implementation, AI policy scrutiny, AI Research, AI safety research, AI slop, Artificial Intelligence, automation, binding frameworks, civil society, compute efficiency, cyber geographies, DeepSeek R1, digital age, digital splintering, distributed AI communities, enterprise AI, Ethical AI, Explainable AI, FAAMG, federalized AI, Generative AI, global economy, government regulation, inclusive AI solutions, India Inc., Indian Society of Artificial Intelligence and Law, industry forums, International Algorithmic Law, Jevons Paradox, labor unions, lawyer associations, multidisciplinary AI, multipolar world, non-binding frameworks, normative emergence, normative practices, open source communities, professional associations, public international law, RBI FREE-AI Committee, risk appetite, SEO, social media, SOLAIR Conference, space law, telecommunications law, trade unions, Vienna Convention on Diplomatic Relations, YMANGA Related Terms in Techindata.in Explainers Definitions - A - E applicationgiri AI Doomerism AI Psychosis AI Washing Agent Debt All-Comprehensive Approach App Crappers Artificial Intelligence Hype Cycle Automation CEI Classification Chain-of-Thought Prompting Data-related Definitions in DPDPA Distributed Ledger Ethics-based concept classification Definitions - F - J Framework Fatigue GaryMarcus'd 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) Generative AI applications with a collection of standalone use cases related to one another (GAI2) Indo-Pacific Intended Purpose / Specified Purpose Jurisprudential Compression Tax International Algorithmic Law Issue-to-issue concept classification Information Cosplay Definitions - K - P Klarna Effect Language Model Manifest Availability Mixture-of-Experts (MoE) Multi-alignment Model Algorithmic Ethics standards (MAES) Multipolar World Multipolarity Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Neurosymbolic AI Object-Oriented Design Omnipotence Omnipresence Performance Effect Permeable Indigeneity in Policy (PIP) Phenomena-based concept classification Privacy by Default Privacy by Design Proprietary Information Definitions - Q - U Roughdraft AI SOTP Classification Strategic Autonomy Strategic Hedging Technical concept classifcation Techno-Legal Measures (DPDP Rules + DPDPA) Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Toolware Transformer Model Definitions - V - Z WANA WENA Whole-of-Government Response Zero Knowledge Systems Related Articles in Techindata.in Insights 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 4 Insight(s) on Government Affairs 2 Insight(s) on AI and Intellectual Property Law . Previous Item Next Item

Section 5 – Technical Methods of Classification PUBLISHED Previous Next Section 5 – Technical Methods of Classification (1) These methods as designated in clause (ii) of sub-section (1) of Section 3 classify artificial intelligence technologies subject to their scale, inherent purpose, technical features and technical limitations such as – (i) General Purpose Artificial Intelligence Applications with Multiple Stable Use Cases (GPAIS) as described in sub-section (2); (ii) General Purpose Artificial Intelligence Applications with Multiple Short-Run or Unclear Use Cases (GPAIU) as described in sub-section (3); (iii) Specific-Purpose Artificial Intelligence Applications with One or More Associated Standalone Use Cases or Test Cases (SPAI) as described in sub-section (4); (2) General Purpose Artificial Intelligence Systems with Multiple Stable Use Cases (GPAIS) are classified based on a technical method that evaluates the following factors in accordance with relevant sector-specific and sector-neutral industrial standards: (i) Scale: The ability to operate effectively and consistently across a wide range of domains, handling large volumes of data and users. (ii) Inherent Purpose: The capacity to be adapted and applied to multiple well-defined use cases within and across sectors. (iii) Technical Features: Robust and flexible architectures that enable reliable performance on diverse tasks and requirements. (iv) Technical Limitations: Potential challenges in maintaining consistent performance and compliance with sector-specific regulations across the full scope of intended use cases. Illustration An AI system used in healthcare for diagnostics, treatment recommendations, and patient management. This AI consistently performs well in various healthcare settings, adhering to medical standards and providing reliable outcomes. It is characterized by its large scale in handling diverse medical data and serving multiple institutions, its inherent purpose of assisting healthcare professionals in decision-making and care improvement, robust technical architecture and accuracy while adhering to privacy and security standards, and potential limitations in edge cases or rare conditions. (3) General Purpose Artificial Intelligence Systems with Multiple Short-Run or Unclear Use Cases (GPAIU) are classified based on a technical method that evaluates the following factors in accordance with relevant sector-specific and sector-neutral industrial standards: (i) Scale: The ability to address specific short-term needs or exploratory applications within relevant sectors at a medium scale. (ii) Inherent Purpose: Providing targeted solutions for emerging or temporary use cases, with the potential for future adaptation and expansion. (iii) Technical Features: Modular and adaptable architectures enabling rapid development and deployment in response to evolving requirements. (iv) Technical Limitations: Uncertainties regarding long-term viability, scalability, and compliance with changing industry standards and regulations. Illustration An AI system used in experimental smart city projects for traffic management, pollution monitoring, and public safety. Deployed at a medium scale in specific locations for limited durations, its inherent purpose is testing and validating AI feasibility and effectiveness in smart city applications. It features a modular, adaptable technical architecture to accommodate changing requirements and infrastructure integration, but faces potential limitations in scalability, interoperability, and long-term performance due to the experimental nature. (4) Specific-Purpose Artificial Intelligence Systems with One or More Associated Standalone Use Cases or Test Cases (SPAI) are classified based on a technical method that evaluates the following factors: (i) Scale: The ability to address specific, well-defined problems or serve as proof-of-concept implementations at a small scale. (ii) Inherent Purpose: Providing specialized solutions for individual use cases or validating AI technique feasibility in controlled environments. (iii) Technical Features: Focused and optimized architectures tailored to the specific requirements of the standalone use case or test case. (iv) Technical Limitations: Constraints on generalizability, difficulties scaling beyond the initial use case, and challenges ensuring real-world robustness and reliability. Illustration An AI chatbot used by a company for customer service during a product launch. As a small-scale standalone application, its inherent purpose is providing automated support for a specific product or service. It employs a focused, optimized technical architecture for handling product-related queries and interactions, but faces limitations in handling queries outside the predefined scope or adapting to new products without significant modifications. Related Indian AI Regulation Sources

Context Window The Indic Pacific Glossary The Complete Glossary Context Window Date of Addition 17 Oct 2025 The maximum number of tokens (words, subwords, or characters) that a language model can process simultaneously as input and maintain in working memory when generating responses. Context window size represents a fundamental technical constraint that determines an LLM's ability to reason over long documents, maintain conversation history, or incorporate retrieved information in RAG systems. Expansion of context windows from thousands to millions of tokens has become a key competitive dimension in LLM development, though larger windows incur quadratic computational costs and do not guarantee improved reasoning quality. Related Long-form Insights on IndoPacific.App The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Previous Term Next Term terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

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

Generative AI applications with a collection of standalone use cases related to one another (GAI2) The Indic Pacific Glossary The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com Generative AI applications with a collection of standalone use cases related to one another (GAI2) Date of Addition 26 April 2024 This is an ontological sub-category of Generative AI applications. Such Generative AI Applications have more than one standalone use cases, which are related to one another. The best example of such a Generative AI Application is that of GPT-4's recent update, which can create text and images based on human prompts, and modify them as per requirements. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Learn More [Version 1] A New Artificial Intelligence Strategy and an Artificial Intelligence (Development & Regulation) Bill, 2023 Learn More [Version 2] Draft Artificial Intelligence (Development & Regulation) Act, 2023 Learn More [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Learn More AIACT.IN Version 3 Quick Explainer Learn More Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Learn More Sections 4-9, AiACT.IN V4 Infographic Explainers Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Previous Term Next Term