Search Results

Digital Colony Risk Explainers The Complete Glossary Digital Colony Risk Date of Addition 20 Feb 2026 The condition in which a politically sovereign state becomes progressively dependent on foreign-owned digital infrastructure, platforms, or AI systems — not through any discrete act of subjugation but through the incremental accrual of technical, economic, and regulatory concessions that, in aggregate, transfer effective control over the state's data economy and technological development to external corporate or state actors. The risk is characterised by its gradual onset: each individual dependency appears containable in isolation, while the cumulative structure renders domestic sovereignty increasingly nominal. The condition is most acute where the available remedies are themselves structurally compromised: judicial mechanisms may find jurisdictional reach limited by the corporate architecture of foreign platforms, while executive instruments capable of compelling compliance tend to operate outside the framework of independent oversight — resolving the accountability gap against the platform, without necessarily resolving it in favour of the citizen. In either case, the locus of effective control remains external to, or unmediated by, the ordinary legal and democratic institutions of the state. Distinguished from formal colonialism by its operation through market mechanisms, contractual architecture, and institutional asymmetry rather than territorial control or legal compulsion. Related Long-form Insights on IndoPacific.App Averting Framework Fatigue in AI Governance [IPLR-IG-013] 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 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 8 – Prohibition of Unintended Risk AI Systems PUBLISHED Previous Next Section 8 - Prohibition of Unintended Risk AI Systems The development, deployment, and use of unintended risk AI systems, as classified under the sub-section (5) of Section 7, is prohibited. Related Indian AI Regulation Sources Ferid Allani v. Union of India & Ors., W.P.(C) 7/2014 (Delhi High Court, Dec 12, 2019) December 2019 Jaswinder Singh @ Jassi v. State of Punjab & Anr., CRM-M-22496-2022, order dated 27-3-2023 March 2023 Md Zakir Hussain v. State of Manipur, W.P. (C) No. 1080 of 2023 (Manipur High Court, May 23, 2024) May 2024

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. :) South Asian Review of International Law, Volume 1 Get this Publication 2020 ISBN 978-81-947926-1-1 Author(s) Abhivardhan, Akash Manwani, Alexandru George Mos, Amulya Anil, Daniel Fitzgerald, Deeksha Prakash, Garima Ojha, Mehreen Mander, Nikhil Dongol, Pranshu Gupta, Pratham Sharma, Punishk Handa, R Kavipriyan, Saloni Subanshi, Shamshir Malik, Sulekha Agarwal, Udomo Ali, Vishaka Ramesh, Vishesh Bhatia Editor(s) Abhivardhan, Aryakumari Sailendraja, Bulbul Khaitan, Nikhil Dongol, Sulekha Agarwal, Udomo Ali IndoPacific.App Identifier (ID) SARIL1 Tags Comparative Law., Conflict Resolution, Diplomacy, Governance, Human Rights, International Law, Legal Studies, Policy, Review, South Asia 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

We offer publication services for our in-house research projects and external publications, such as books, collection of research works and other academic and industry-related publications in the fields of law and policy. Indic Pacific Publishing Ask for Quote IPP is the Publication Division of Indic Pacific Legal Research LLP. We offer publication services for our in-house research projects and external publications, such as books, collection of research works and other academic and industry-related publications in the fields of law and policy. Search our publications at Go to VLiGTA.App Content we publish Books Monographs, or general works of research or opinion in the fields of law and policy. Technical Reports Reports covering essential research questions and issues of importance, in the fields of law and policy. Collections of Research Works Collections of research papers, reports and commentaries in the fields of law and policy. Handbooks Handbooks, which cover integral concepts and phenomena in the fields of law and policy. benefits of publishing with us Proof-reading and Plagiarism Checking We offer proof-reading services for every manuscript, which includes plagiarism check services. Although, it is subject to cooperation with every editor/author to ensure that the corrections are made promptly to preserve the quality of the work, we are hospitable to provide proof reading and plag check services for our publications. Marketing and Brand Building Support We offer every manuscript proof-reading services, which includes plagiarism check services. Although, it is subject to cooperation with every editor/author to ensure that the corrections are made promptly to preserve the quality of the work, we are hospitable to provide proof reading and plag check services for our publications. Marketing & Brand Building Support We understand that every publication, be it a report, a book, a handbook or a collection of research papers, for example, is a work, which clearly represents the academic and industrial potential of the authors. We provide tailor-made solutions to build and cultivate the branding of the authors and editors associated with the publications and consult them. Content Marketing Funnel We also offer content marketing services through multiple means, to promote the chapters of the publications, or any short article/blog authored by any of the authors and editors, related to their publications. We are also open to curate specific solutions on content marketing based on the authors and editors' brand development & growth. pricing & specifics Ask for Quote Terms of Royalty Tier-1 For Single or Multiple Authors (1-3 authors only) Tier-2 In-house publications and publications based on external partnerships for reports, handbooks, briefs and other documents of academic, industrial and policy importance under the Vidhitsa Law Institute of Global and Technology Affairs Tier-3 Conference proceedings and collections of research works

Section 21-A – Data Classification and Localisation Requirements PUBLISHED Previous Next Section 21-A – Data Classification and Localisation Requirements (1) The Central Government shall establish a data classification and tiering system that defines storage, access, and transfer requirements based on data sensitivity and strategic importance. The system shall include the following tiers: (i) Tier 1: Critical National Security Data (a) Characteristics: Includes data with direct national security implications, sensitive government infrastructure data, critical defence information, and biometric/sensitive personal identification data. (ii) Tier 2: Strategic Sectoral Data (a) Strategic Sectors Designated: (i) Healthcare (ii) Financial Services (iii) Critical Infrastructure, and (iv) Emerging Technology Research (iii) Tier 3: Commercial and Research Data (a) Characteristics: Includes non-sensitive commercial data, academic and research collaboration data, and open-source AI training datasets. (2) To promote responsible data management and adherence to localisation requirements among companies, the Central Government shall provide incentives aligned with the entity’s AI classification under Chapter II. Incentives include: (i) Tax Benefits: Available for entities compliant with localisation protocols, with additional consideration given based on the AI system’s classification type under the commercial methods of classification in Section 6. (ii) Expedited Cross-Border Approvals: Reserved for institutions with demonstrated responsible cross-border data management, particularly those operating high-risk AI systems or classified under AI-IaaS and AI-Com as per methods of classification in Section 5 due to their integration with sensitive digital infrastructure. (iii) Recognition Certificates for Exemplary Management Practices: Granted to institutions that demonstrate best practices in data management, security, and AI governance, taking into account methods of classification in Sections 5 and 7. (3) The framework shall be rolled out in phases over 24 months and include: (i) Regular review and recalibration to adapt to emerging technological and policy challenges. (ii) Stakeholder consultation mechanisms to incorporate feedback from industry, academia, and government entities. (iii) Capacity building programs to support entities in implementing and maintaining compliance with these standards. Related Indian AI Regulation Sources Guidelines on Responsible Use of Artificial Intelligence and Machine Learning in Securities Markets June 2025

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

In-context Learning Explainers The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com In-context Learning Date of Addition 26 April 2024 In-context learning for generative AI is the ability of a generative AI model to learn and adapt to new information based on the context in which it is used. This allows the model to generate more accurate and relevant results, even if it has not been specifically trained on the specific task or topic at hand. For example, an in-context learning generative AI model could be used to generate a poem about a specific topic, such as "love" or "nature." The model would be provided with a few examples of poems about the selected topic, which it would then use to understand the context of the task. The model would then generate a new poem about the topic that is consistent with the context. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Related Long-form Insights on IndoPacific.App Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Learn More Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Learn More Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 Learn More The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More Previous Term Next Term

Semi-Supervised Learning Date of Addition 22 March 2025 A machine learning approach that combines supervised and unsupervised techniques by training models on a mix of labeled and unlabelled data. This method leverages the structure in unlabelled data to improve generalisation while using limited labeled examples for guidance. Semi-supervised learning encompasses several methodologies including self-training (using confident predictions on unlabeled data to expand the training set), co-training (using multiple models trained on different feature subsets), multi-view training (using different data representations), and graph-based approaches that propagate labels through similarity networks. Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Previous Term Next Term Explainers The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

Released on January 23, 2026, by the Office of the Principal Scientific Adviser to the Government of India (Prof. Ajay Kumar Sood), this is the second white paper in the "Emerging Policy Priorities for India's AI Ecosystem" series. Strengthening AI Governance Through Techno-Legal Framework (White Paper, Part 2 of Emerging Policy Priorities Series) Released on January 23, 2026, by the Office of the Principal Scientific Adviser to the Government of India (Prof. Ajay Kumar Sood), this is the second white paper in the "Emerging Policy Priorities for India's AI Ecosystem" series. 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. January 2026 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 Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] 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 Related draft AI Law Provisions of aiact.in Section 11 – Registration & Certification of AI Systems Section 11 – Registration & Certification of AI Systems Section 12 – National Registry of Artificial Intelligence Use Cases Section 12 – National Registry of Artificial Intelligence Use Cases Section 13 – National Artificial Intelligence Ethics Code Section 13 – National Artificial Intelligence Ethics Code Section 14 – Model Standards on Knowledge Management Section 14 – Model Standards on Knowledge Management 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

Hierarchical Feedback Distortion 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 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

Bombay High Court March 2025 interim order protecting filmmaker's personality rights against unauthorized name use in film title "Shaadi Ke Director Karan Aur Johar." Karan Johar v. India Pride Advisory Private Ltd. & Ors. ("Shaadi Ke Director Karan Aur Johar"), COM IPR Suit (L.) No. 17863/2024, Bombay High Court, Order dated March 7, 2025 Bombay High Court March 2025 interim order protecting filmmaker's personality rights against unauthorized name use in film title "Shaadi Ke Director Karan Aur Johar." Previous Next The AIACT.IN India AI Regulation Tracker This is a simple regulatory tracker consisting all information on how India is regulating artificial intelligence as a technology, inspired from a seminal paper authored by Abhivardhan and Deepanshu Singh for the Forum of Federations, Canada, entitled, "Government with Algorithms: Managing AI in India’s Federal System – Number 70 ". We have also included case laws along with regulatory / governance documents, and avoided adding any industry documents or policy papers which do not reflect any direct or implicit legal impact. March 2025 Read the Document Issuing Authority Bombay High Court Type of Legal / Policy Document Judicial Pronouncements - National Court Precedents Status In Force Regulatory Stage Regulatory Binding Value Legally binding instruments enforceable before courts AIACT. Regulation Visualiser Find more sources Related Long-form Insights on IndoPacific.App Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Related draft AI Law Provisions of aiact.in Section 21 – Intellectual Property Protections Section 21 – Intellectual Property Protections Section 23 – Content Provenance and Identification Section 23 – Content Provenance and Identification

Section 9 – High-Risk AI Systems in Strategic Sectors PUBLISHED Previous Next Section 9 - High-Risk AI Systems in Strategic Sectors (1) The Central Government shall designate strategic sectors where the development, deployment, and use of high-risk AI systems shall be subject to sector-specific standards and regulations, based on the risk classification methods outlined in Chapter II of this Act. (2) In the event of any conflict between the provisions of this Act and sector-specific regulations concerning high-risk AI systems in strategic sectors, the provisions of this Act shall prevail, unless otherwise specified. Related Indian AI Regulation Sources Responsible AI #AIforAll (Discussion Paper on Facial Recognition Technology) November 2022