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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. :) The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Get this Publication 2024 ISBN 978-81-977227-9-0 Author(s) Abhivardhan, Alisha Garg, Samyak Deshpande, Sanvi Zadoo Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-IG-008 Tags Abhivardhan, advanced AI, AI safety, AI Seoul Summit, AI Technology, Anthropic, Artificial Intelligence, Bletchley Summit, complexity, Development, economic perspective, emerging class, examines, extraction, features, focuses, follow-up, Generative AI, global artificial intelligence ecosystem, government, International Scientific Report on the Safety of Advanced AI, interpretable features, large language models, legal-ethical perspective, LLMs, monosemanticity, neurons, neurosymbolic AI, polysemantic neurons, pre-regulatory ethical considerations, proposes, report, research paper, safety, stakeholders, technical perspective, United Kingdom Related Terms in Techindata.in Explainers Definitions - A - E AI Agents AI Knowledge Chain AI Literacy Context Window 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 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

Section 20-A – Transparency and Accountability in AI-related Government Initiatives and Public-Private Partnerships PUBLISHED Previous Next Section 20A – Transparency and Accountability in AI-related Government Initiatives and Public-Private Partnerships (1) This section applies to all AI-related initiatives undertaken by any governmental body, statutory authority, public sector entity, or public-private partnership (PPP) involving AI technologies for public services or infrastructure. (2) Transparency Requirements: All entities under this section must comply with the Right to Information Act, 2005, by publicly disclosing the following information about AI initiatives: (i) A clear statement of the project’s purpose and expected outcomes; (ii) Details of funding, including public funds, subsidies, or PPP financial arrangements; (iii) Summaries of risk assessments addressing privacy, security, and ethical impacts; (iv) Descriptions of algorithms used in decision-making for public services, including their purpose and functionality; (v) Key performance indicators (KPIs) to evaluate the AI system’s effectiveness. (3) Additional Obligations for Public-Private Partnerships (PPPs): PPPs involving AI technologies must: (i) Disclose key contractual terms, including payment structures, risk allocation, and responsibilities of each party; (ii) Provide public access to data generated by AI systems in public service contexts, unless restricted under Section 8 of the RTI Act, 2005, or Section 6 of the DPDP Act, 2023; (iii) Conduct annual independent audits to verify compliance with ethical standards and performance metrics, and publish the audit results. (4) Algorithmic Accountability: AI systems used in government or PPP initiatives that impact individuals’ rights or access to public services must: (i) Provide written explanations of algorithmic decisions upon request by affected individuals; (ii) Document and disclose measures to prevent algorithmic bias, including details of data selection and validation processes; (iii) Conduct and publish impact assessments before deployment, evaluating risks to vulnerable populations. (5) Before launching large-scale AI projects or entering PPPs involving AI, the responsible government body must: (i) Hold public consultations with stakeholders, including civil society, industry experts, academics, and affected communities; (ii) Publish a summary of consultation feedback and explain how it was incorporated into the project plan. (6) All entities under this section must submit an annual report to the Indian Artificial Intelligence Council (IAIC), which must be published on official government websites, detailing: (i) Progress on AI projects; (ii) Results of audits or impact assessments; (iii) Incidents of AI misuse or failure, with corrective actions taken; (iv) Measures implemented to address transparency, accountability, and ethical concerns. (7) Exemptions: Information may be withheld from disclosure if it: (i) Compromises national security; (ii) Violates data protection rights of data principals under the Digital Personal Data Protection Act, 2023; (iii) Interferes with ongoing investigations or enforcement actions; (iv) Conflicts with legitimate use purposes as defined under Section 6 of the DPDP Act, 2023, per Section 8 of the Right to Information Act, 2005. Related Indian AI Regulation Sources

Language Model Date of Addition 22 March 2025 An AI algorithm that uses deep learning techniques and large datasets to understand, summarise, generate, and predict text-based content. Large language models (LLMs) dramatically expand this capability through transformer architectures and massive parameter counts. Modern language models, particularly LLMs, are trained on vast corpora of text data through multiple training stages, typically starting with unsupervised learning on unstructured data followed by fine-tuning with self-supervised learning. They employ transformer neural networks with self-attention mechanisms to understand relationships between words and concepts. This architecture enables them to assign weights to different tokens to determine contextual relationships. Related Long-form Insights on IndoPacific.App Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] 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 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 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 Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More 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 24-A – Right to Artificial Intelligence Literacy PUBLISHED Previous Next Section 24-A – Right to Artificial Intelligence Literacy (1) Every individual has the right to basic artificial intelligence literacy that enables meaningful participation in an AI-augmented society. (2) For the purpose of this section, “artificial intelligence literacy” shall include: (i) Knowledge of fundamental AI concepts, capabilities, and limitations; (ii) Understanding of how AI systems may impact individual rights, including privacy, autonomy, and equal treatment; (iii)Ability to identify AI-generated content and understand AI involvement in automated decision-making processes; (iv) Awareness of mechanisms to seek recourse when adversely affected by AI systems. (3) Educational institutions receiving public funding shall progressively integrate age-appropriate AI literacy into their curricula within three years of the commencement of this Act. (4) All public services utilizing AI systems shall provide accessible information about: (i) The fact of AI deployment in service delivery; (ii) How the AI system influences decisions or outcomes; (iii)Options available to citizens who wish to opt for human review or intervention. Related Indian AI Regulation Sources

AI as an Industry Explainers 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 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

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 Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Get this Publication 2022 ISBN 978-81-957087-9-6 Author(s) Abhivardhan, Bhavana J Sekhar, Poulomi Chatterjee Editor(s) Not Applicable IndoPacific.App Identifier (ID) VLiGTA-TR-001 Tags Artificial Intelligence hype, Artificial Intelligence Resilience, competition policy, credibility concerns, D9 group, data privacy, demand, digital economy, economic innovations, ethical innovations., ethics research, Indian Society of Artificial Intelligence and Law, information economy, interconnectedness, perceptions, stocks, technology companies, valuations, Vidhitsa Law Institute of Global and Technology Affairs Related Terms in Techindata.in Explainers Definitions - A - E AI as an Entity AI Doomerism AI Washing AI-based Anthropomorphization Accountability Algorithmic Activities and Operations All-Comprehensive Approach Artificial Intelligence Hype Cycle Automation Class-of-Applications-by-Class-of-Application (CbC) approach 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 a collection of standalone use cases related to one another (GAI2) Intended Purpose / Specified Purpose Information Cosplay Definitions - K - P Klarna Effect Language Model Manifest Availability Model Algorithmic Ethics standards (MAES) Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Parameters Polyvocality Privacy by Default Privacy by Design Proprietary Information Definitions - Q - U Roughdraft AI SOTP Classification Semi-Supervised Learning Strategic Autonomy Synthetic Content Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Token Economics Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 7 Insight(s) on AI and Competition Law 5 Insight(s) on digital competition law . Previous Item Next Item

Section 11 – Registration & Certification of AI Systems PUBLISHED Previous Next Section 11 – Registration & Certification of AI Systems (1) The IAIC shall establish a voluntary certification scheme for AI systems based on their industry use cases and risk levels, on the basis of the means of classification set forth in Chapter II. The certification scheme shall be designed to promote responsible AI development and deployment. (2) The IAIC shall maintain a National Registry of Artificial Intelligence Use Cases as described in Section 12 to register and track the development and deployment of AI systems across various sectors. The registry shall be used to inform the development and refinement of the certification scheme and to promote transparency and accountability in artificial intelligence governance. (2) The certification scheme shall be based on a set of clear, objective, and risk-proportionate criteria that assess the inherent purpose, technical characteristics, and potential impacts of AI systems. (3) AI systems classified as narrow or medium risk under Section 7 and AI-Pre under sub-section (8) of Section 6 may be exempt from the certification requirement if they meet one or more of the following conditions: (i) The AI system is still in the early stages of development or testing and has not yet achieved technical or economic thresholds for effective standardisation; (ii) The AI system is being developed or deployed in a highly specialized or niche application area where certification may not be feasible or appropriate; or (iii) The AI system is being developed or deployed by start-ups, micro, small & medium enterprises, or research institutions. (4) AI systems that qualify for exemptions under sub-section (3) must establish and maintain incident reporting and response protocols specified in Section 19. Failure to maintain these protocols may result in the revocation of the exemption. (5) Applicability of Section 4 Classification Methods: (i) The conceptual methods of classification outlined in Section 4 are intended for consultative and advisory purposes only. Their application is not mandatory for the National AI Registry of Use Cases under this Section. The IAIC is empowered to: (a) Issue advisories, clarifications, and guidance documents on the interpretation and application of the classification methods outlined in Section 4. (b) Provide sector-specific recommendations for the voluntary use of these classification methods by stakeholders, including developers, regulators, and industry professionals. (c) While these classification methods are not mandatory, stakeholders are encouraged to adopt them on a self-regulatory basis. Voluntary application of these methods can help: (i) Enhance transparency in AI development. (ii) Promote responsible AI deployment across sectors. (iii) Facilitate alignment with ethical standards outlined in the National Artificial Intelligence Ethics Code (NAIEC) under Section 13. (ii) The IAIC may periodically review and update its advisories, clarifications and guidance documents to reflect advancements in AI technologies and emerging best practices, ensuring that stakeholders have access to the latest guidance for applying these conceptual methods. (6) Notwithstanding anything contained in sub-section (5), entities registering high-risk AI systems as defined in the sub-section (4) of Section 7 and those associated with strategic sectors as specified in Section 9 must apply the conceptual classification methods outlined in Section 4. (7) The certification scheme and the methods of classification specified in Chapter II shall undergo periodic review and updating every 12 months to ensure its relevance and effectiveness in response to technological advancements and market developments. The review process shall include meaningful consultation with sector-specific regulators and market stakeholders. Related Indian AI Regulation Sources Principles for Responsible AI (Part 1) February 2021 Operationalizing Principles for Responsible AI (Part 2) August 2021 Fairness Assessment and Rating of Artificial Intelligence Systems (TEC 57050:2023) July 2023 The Ethical Guidelines for Application of AI in Biomedical Research and Healthcare March 2023 Policy Regarding Use of Artificial Intelligence Tools in District Judiciary July 2025 Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee Report August 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. :) India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] Get this Publication 2022 ISBN 978-81-957087-7-2 Author(s) Abhivardhan, Poulomi Chatterjee Editor(s) Not Applicable IndoPacific.App Identifier (ID) GLA-TR-003 Tags Diplomacy, Economics, Global Governance, Governance, India, Indo-Pacific, Innovation, International Relations, Legal Studies, 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

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. :) Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Get this Publication 2025 ISBN 978-81-977227-0-7 Author(s) Abhivardhan, Gargi Mundotia, Sneha Binu, Yashita Parashar Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-IG-014 Tags Abhivardhan, AI Ethics, Algorithmic Trading, Artificial Intelligence, Blockchain, digital economy, Distributed Ledger Technology, Financial Automation, Future of Legal Profession, India, indic pacific legal research, ISAIL, Law Students, Legal Education, Policy, Regulatory Challenges, Supply Chain Management, Technology Governance Related Terms in Techindata.in Explainers Definitions - A - E AI as an Industry AI as a Legal Entity AI as a Subject AI as a Third Party AI Red Teaming AI-based Anthropomorphization Accountability Anthropomorphism-based concept classification Consent Manager (DPDPA) Context Window Data-related Definitions in DPDPA Definitions - F - J Intended Purpose / Specified Purpose Definitions - K - P Language Model Manifest Availability Mixture-of-Experts (MoE) 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 Techno-Legal Measures (DPDP Rules + DPDPA) 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 17 Insight(s) on law and AI 13 Insight(s) on artificial intelligence ethics 9 Insight(s) on artificial intelligence hype 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 7 Insight(s) on RBI FREE-AI Committee 5 Insight(s) on AI Governance 4 Insight(s) on Government Affairs 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 1 Insight(s) on IndiaAI 1 Insight(s) on India-US Relations 1 Insight(s) on responsibility . Previous Item Next Item

Liked our Work? Search it now on IndoPacific.App Get Searching Our Research Know more about our Knowledge Base, years of accumulated and developed in-house research at Indic Pacific Legal Research. Search our Research Treasure on IndoPacific.App. :) Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Get this Publication 2022 ISBN 978-81-957087-4-1 Author(s) Manohar Samal, Poulomi Chatterjee Editor(s) Not Applicable IndoPacific.App Identifier (ID) ISAIL-TR-002 Tags Artificial Intelligence, Data Science, Governance, India, Innovation, Legal Studies, Policy, Regulatory Sandboxes, Techno-Legal Approaches, Technology 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 Language Model Manifest Availability Model Algorithmic Ethics standards (MAES) Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Object-Oriented Design Proprietary Information Definitions - Q - U Roughdraft AI SOTP Classification Synthetic Content Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 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

Algorithmic Activities and Operations Explainers The Complete Glossary Algorithmic Activities and Operations Date of Addition 26 Apr 2024 It refers to the dual functional capacities of algorithms within AI systems or machine-learning frameworks, as understood within a procedural and legal context. Activities encompass the routine, foundational, and general-purpose tasks that algorithms perform, such as data processing, pattern recognition, or automated responses, which are essential for the day-to-day functioning of digital systems across diverse applications. Operations, in contrast, denote specialised, context-driven, or technology-specific tasks that are tailored to particular domains, objectives, or technical environments, such as predictive modelling for financial markets, real-time decision-making in autonomous systems, or adaptive learning in personalised healthcare solutions, for instance. This distinction highlights the layered complexity of algorithmic behaviour, recognising that algorithms operate at varying levels of abstraction and intent, necessitating nuanced governance approaches in a globalised digital ecosystem. This idea was originally proposed in Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022). Original Definition in line with technical report "Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022)": It means the algorithms of any AI system or machine-learning-based system are capable to perform two kinds of tasks, in a procedural sense of law, i.e., performing normal and ordinary tasks - which could be referred to as 'activities' and methodical and context-specific or technology-specific tasks, called 'operations'. Related Long-form Insights on IndoPacific.App Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Artificial Intelligence and Policy in India, Volume 4 [AIPI-V4] 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 Artificial Intelligence and Policy in India, Volume 6 [AIPI-V6] 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. :) Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 Get this Publication 2023 ISBN 978-81-959932-5-3 Author(s) Abhivardhan, Akash Manwani Editor(s) Not Applicable IndoPacific.App Identifier (ID) VLiGTA-TR-005 Tags Abhivardhan, Artificial Intelligence and Law, Artificial Intelligence and Policy in India, EU AI Act, European regulation, India AI, Indo-Pacific, Western Europe and North America 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 Data-related Definitions in DPDPA 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 Inference Latency 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 Techno-Legal Measures (DPDP Rules + DPDPA) 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