AI System

WHAT IS AN 'ARTIFICIAL INTELLIGENCE SYSTEM'

An artificial intelligence (AI) system is a machine‑based system that, for one or more human‑defined objectives, takes inputs such as data, sensor signals, or user instructions and infers how to generate outputs like predictions, content, recommendations, or decisions that can influence physical or virtual environments. AI systems can operate with different levels of autonomy, from tools that only assist humans to systems that act with limited human intervention, and many can adapt or improve their behavior over time based on new data.

The AI system in technology law is closely linked with the debates around data governance and liability. Autonomy is always a big factor that is widely present in these debates because for the AI system to evolve, it needs operational parameters that are based upon its training and the data that are being provided and fed to the AI system, due to which it has limited human oversight to go through the framework designated for its deterministic software. This is the main reason why global networks and governance systems are seeking a precise legal definition that could establish the boundaries within which AI systems can be properly defined, helping assign regulatory obligations to developers and deployers when creating or improving their AI systems.

AI systems are encompassed in a broad spectrum of various technologies. It includes everything from simple predictive algorithms used for content recommendation to autonomous decision-making systems, which are often used in credit scoring (banking industry) and medical diagnostics (healthcare sector). The AI system also has its use in the complex natural language processing models (LLMs) for producing the solutions for complex problems.

Unlike the software technology, which follows a fixed system of explicitly programmed rules, the AI system runs on a unique pattern of learning the patterns from the data and improves continuously, without the necessity of reprogramming it after every prompt and usage of the system.   The AI systems are categorized for operation into two main phases:

1. Training Phase: The AI system is given large amounts of data, like structured data (spreadsheets, databases, and CSV files); unstructured data (the free-form content that is available on the internet at large); and finally, semi-structured data (JSON, XML, and emails with tags). Through this process the algorithms identify patterns that adjust the internal parameters, which are pivotal for the optimization of goals. There are various techniques under which this date is amalgamated for the AI system:
   • Supervised training, which labels the present data for further classification for the system to observe and learn.
   • Unsupervised training, the process of finding patterns
   • Reinforcement learning, the process of trial and error with rewards (meaning more data and algorithms being provided to the AI system).
2. Deployment Phase—The trained AI system/model is provided with new inputs and data, which helps it generate more realistic outputs. The modern AI systems are able to adapt post-deployment through the use of techniques like fine-tuning or online learning.

At the very core of the AI system lies machine learning, which is overseen by soft computing (neural networks and machine logic). Through this, the AI system accepts the uncertainty and approximation instead of demanding exact logical arguments, making the production of solutions a much faster and easier method. Hardware acceleration (GPUs and TPUs) enables the processing of big datasets as well as sophisticated models, such as transformers. Key components of the AI system Just like software, the AI systems are incomplete without their key components:

• Data Layer: Sources, both structured and unstructured; data ingestion procedures; cleaning; and storage make up the data layer. The fuel is diverse, high-quality data
• Data Processing: Transformation, feature extraction, and vector embedding pipelines are examples of data engineering and processing. These are used for semantic search in generative systems.
• Core Algorithm: Machine learning and deep learning models, such as LLMs, CNNs for vision, and RNNs or transformers for language, are developed using basic algorithms. This includes knowledge bases and reasoning engines.
• Orchestration and Inference Layer: Frameworks for multi-step reasoning, chain models like LangChain, workflow management, and large-scale output delivery.
• Cloud or edge servers, GPUs or TPUs, storage, networking, and MLOps tools are examples of hardware and infrastructure for deployment, monitoring, versioning, and scaling.
• Perception and Actuation in Embodied Systems: The actuators or API providers output actions, while the sensor technology, such as the cameras, microphones, and LiDAR, gathers important informational input. The mechanism for monitoring of bias, drift, security, explainability, and compliance is part of the governance and safety layer. This is especially important in regulated situations.

The AI System is further classified into the high-risk AI systems (for further information, you can refer it to this page).

India's approach to classifying and regulating high-risk AI systems diverges meaningfully from the European Union's four-tier risk pyramid established under the EU AI Act 2024. India's risk assessment and classification system focuses on national security issues and harms that may be caused to vulnerable groups, including deepfakes aimed at women, child safety, language bias, and caste bias, rather than relying on generic risk grids. This context-sensitive model is seen as a better fit for India's societal complexity and demographic diversity than universalist frameworks imported from the Global North.

India's AI Governance Guidelines identify the general risks of AI as falling into categories of malicious use (such as misrepresentation through deepfakes), algorithmic discrimination, lack of transparency, systemic risks, loss of control, and threats to national security risks either created or exacerbated by AI systems. Crucially, an India-specific risk assessment framework, based on empirical evidence of harm, is viewed as critical, alongside industry-led compliance efforts and a combination of different accountability models. AI-driven products and services that cause harm to consumers may attract liability under the Consumer Protection Act. The Central Consumer Protection Authority (CCPA) has issued guidelines on misleading advertisements, which are applicable to AI-generated content.

OFFICIAL DEFINITION OF  AI SYSTEMS

The Indian legal jurisprudence and the technology have still not defined ‘artificial intelligence’ or the ‘AI system’ in any act, legislation, rule, notification, or gazette document. India follows a techno-legal principle-based approach where, through government schemes like the India AI Mission and the existing laws IT Act 2000 and the DPDP Act 2023, the fundamentals and the base of such definitions are first made. Authoritative functional descriptions appear in high-level policy guidelines and official white papers issued by MeitY, the Office of the Principal Scientific Adviser (PSA), and the Supreme Court of India. These descriptions are deliberately broad and functional to prevent freezing, rapidly changing technologies.

In law, public policy, and technology governance debates, AI systems raise questions about data governance, liability, and human oversight because their behavior depends heavily on training data and design choices.

Many frameworks describe AI systems across a full lifecycle, typically including design and problem formulation, data collection and preparation, model development and training, deployment and integration into real‑world environments, and ongoing monitoring, maintenance, and eventual decommissioning

AI systems in Legislation

Although "AI" and "AI systems" are not defined in the Digital Personal Data Protection (DPDP) Act, 2023, AI systems are treated as data fiduciaries when they decide how and why to process personal data (such as court records or litigant information). Any AI that processes digital personal data is subject to obligations regarding consent, purpose limitation, data minimization, and accountability. The term "synthetically generated information" (i.e., AI-generated or altered audio/visual content that appears real) is defined in the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (amended 2026), but the underlying AI system is not.

Digital Personal Data Protection (DPDP) Act, 2023

Although 'AI' and 'AI systems' are not explicitly defined in the DPDP Act, 2023, AI systems are treated as data fiduciaries when they decide how and why to process personal data (such as court records or litigant information). Any AI that processes digital personal data is subject to obligations regarding consent, purpose limitation, data minimization, and accountability.

• Sections 8–11: Classify large-scale AI processors as Significant Data Fiduciaries.
• Requirements: Data Protection Impact Assessments (DPIAs), independent audits, and appointment of a Data Protection Officer.
• Direct applicability: Judicial AI tools (SUPACE, SUVAS) that handle case data

Information Technology Act, 2000

Under Sections 43A and 79 and the Amended Intermediary Rules (2026), AI systems that generate or modify content lose intermediary safe harbor protection if they 'initiate transmission, select recipients, or modify data. 'The 2026 amendments require labeling of AI-generated/synthetic content.

IT (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (amended 2026)

The term 'synthetically generated information' (i.e., AI-generated or AI-altered audio/visual content that appears real) is defined here, but the underlying AI system itself is not defined. This creates a regulatory gap where the output is regulated, but the generating tool is not directly addressed.

Telangana AI Framework (2025, updated early 2026)

Telangana has leveraged its robust technology infrastructure—anchored in Hyderabad’s position as a global IT and AI hub—to implement one of India’s most advanced state-level AI governance frameworks. The Government of Telangana’s Information Technology, Electronics & Communications Department published the Telangana AI Framework in mid-2025 and released an updated, enforceable version in February 2026. Unlike the central government’s principle-based Guidelines, the Telangana Framework is a mandatory instrument for state departments and agencies deploying AI systems in public service delivery. The Framework adopts the term “AI for Social Good” not merely as a branding exercise but as the operative criterion for determining which AI systems require prior approval. An “AI system” is defined functionally as “any application that automates, assists, or replaces human decision-making in service of a defined public policy objective, using machine-based inferencing over data.” All such systems must demonstrate a measurable “social good” impact—reduction in maternal mortality, improvement in learning outcomes, efficiency gains in public distribution systems—through an impact assessment conducted prior to deployment.

Tamil Nadu Safe & Ethical Artificial Intelligence Policy 2020

Tamil Nadu was an early mover in AI governance, adopting its Safe & Ethical Artificial Intelligence Policy in 2020—well before the central government’s structured engagement with the topic. The policy remains one of India’s most comprehensive state-level efforts to embed ethics and transparency into AI deployment. The policy, published by the Tamil Nadu e-Governance Agency (TNeGA), defines an “AI system” broadly as “a technological system that uses computational models and algorithms to perform tasks that normally require human intelligence, including but not limited to learning, reasoning, and pattern recognition.” The definition is intentionally inclusive, designed to capture not only machine learning and deep learning models but also earlier generations of rule-based expert systems deployed in government. The Framework adopts the term “AI for Social Good” not merely as a branding exercise but as the operative criterion for determining which AI systems require prior approval. An “AI system” is defined functionally as “any application that automates, assists, or replaces human decision-making in service of a defined public policy objective, using machine-based inferencing over data.” All such systems must demonstrate a measurable “social good” impact—reduction in maternal mortality, improvement in learning outcomes, efficiency gains in public distribution systems—through an impact assessment conducted prior to deployment.

Legal Provisions Relating to ‘AI System’

Despite the deliberate absence of a singular statutory definition of an ‘AI system’ in India—and the diversity of international formulations—a robust legal framework has developed through a constellation of statutory obligations, delegated rules, state-level instruments, and international benchmarks. This section unpacks the principal legal provisions that shape the governance, accountability, and operational contours of AI systems in India and across major jurisdictions.

Digital Personal Data Protection Act, 2023 (DPDP Act)

The DPDP Act, 2023 constitutes the bedrock of personal data protection in India, and its provisions have a direct bearing on AI systems that process digital personal data. The Act does not mention ‘AI’ explicitly, but its definitions of ‘data fiduciary’ and ‘processing’ are sufficiently broad to encompass developers, deployers, and users of AI systems. The most consequential provisions are clustered around the classification of Significant Data Fiduciaries and the attendant compliance obligations.

Section 8: Significant Data Fiduciary

The Central Government may notify any data fiduciary or class of data fiduciaries as a Significant Data Fiduciary (SDF) based on factors including the volume and sensitivity of personal data processed, the risk of harm to data principals, and the potential impact on the sovereignty and integrity of India. AI systems that process personal data at population scale—such as large language models trained on citizen data, algorithmic welfare-distribution platforms, and predictive policing tools—are prime candidates for SDF designation. Once classified as an SDF, the entity must comply with heightened obligations under sections 9–11.

Section 9: Appointment of a Data Protection Officer

An SDF must appoint a Data Protection Officer (DPO) based in India, who acts as the single point of contact for data principals. For AI systems handling judicial data (SUPACE and SUVAS), this requirement would entail designating a DPO responsible for overseeing data-handling practices, responding to grievances related to algorithmic profiling, and ensuring that the principles of purpose limitation and data minimisation are respected during model training and inference.

Section 10: Data Protection Impact Assessment

Section 10 obliges an SDF to conduct a Data Protection Impact Assessment (DPIA) prior to undertaking any processing activity that is likely to result in a high risk to the rights of data principals. The DPIA must describe the nature, scope, context, and purposes of the processing; assess the risks and harms; and specify the measures adopted to mitigate such risks. For an AI system deployed in judicial administration, this assessment would need to examine the risk of re-identification from case data, biased outputs affecting particular castes or communities, and any adverse impact on the right to a fair hearing. The DPIA requirement constitutes a de facto algorithmic impact assessment even though the DPDP Act does not use that terminology.

Information Technology Act, 2000 and the 2026 Intermediary Rules

The Information Technology Act, 2000, though enacted long before the contemporary AI wave, provides a surprisingly resilient framework for regulating AI-generated content and allocating liability.

Section 43A – Compensation for Failure to Protect Data

Section 43A provides that a body corporate that possesses, deals with, or handles any sensitive personal data or information in a computer resource it controls shall be liable to pay compensation if it is negligent in implementing and maintaining reasonable security practices, thereby causing wrongful loss or gain to any person. AI systems that are trained on, or process, sensitive personal data (such as biometric information used in facial recognition systems, or health data used for clinical decision-support) fall squarely within the ambit of this provision. If a hospital deploys an AI diagnostic tool that negligently exposes patient data, the hospital—as the body corporate—faces statutory liability.

Section 79 – Intermediary Safe Harbour

Section 79(1) grants intermediaries exemption from liability for third-party information that they merely transmit, store, or host. However, Section 79(2)(b) carves out a crucial exception: an intermediary is not entitled to the exemption if it “initiates the transmission,” “selects the receiver of the transmission,” or “selects or modifies the information contained in the transmission.”

This statutory language acquires acute significance for AI systems. When an AI model generates text, image, or audio content—whether it is a deepfake, a chatbot response, or a synthetic video—the platform that hosts that output is arguably not a passive intermediary. The act of generation itself constitutes “modification” of information (transforming input data into novel output). Consequently, platforms deploying generative AI tools lose the safe harbour protection that Section 79 otherwise affords, and may be held directly liable for the content produced by their AI systems.

Amendments to the Intermediary Rules

In February 2026, the Ministry of Electronics and Information Technology amended the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021. The amendments introduced two provisions directly aimed at AI:

• Labelling of Synthetic Content: Any intermediary that provides a service involving the creation, modification, or hosting of AI-generated content must prominently label such content as “AI-Generated” or “Synthetic.” The labelling must be machine-readable and remain irrevocably affixed through all subsequent reproductions or modifications. This imposes a mandatory provenance obligation on platforms, aligning with global standards such as the Coalition for Content Provenance and Authenticity (C2PA).

• Removal of Safe Harbour for Unlabelled Synthetic Content: If an intermediary fails to label synthetic content and that content is found to violate any law, the intermediary is deemed to have “selected or modified” the information under Section 79(2)(b) and is stripped of safe harbour protection. This creates a powerful incentive for compliance, particularly for generative AI platforms and social media services where deepfakes proliferate.

AI system in International Instruments

India’s domestic framework is informed by, though not formally bound to, a tapestry of international definitions and governance instruments. The following instruments represent the key global reference points that Indian policymakers and courts are increasingly citing.

EU Artificial Intelligence Act, 2024 (Regulation 2024/1689)

The EU AI Act, which entered into force on 1 August 2024, provides the world’s first comprehensive, legally binding definition of an ‘AI system’ in Article 3(1):

“an AI system means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”

The definition identifies seven constitutive elements: (i) machine-based, (ii) varying levels of autonomy, (iii) optional adaptiveness after deployment, (iv) explicit or implicit objectives, (v) inferencing from input, (vi) outputs (predictions, content, recommendations, decisions), and (vii) influence on physical or virtual environments. The European Commission’s February 2025 Guidelines clarify that the definition adopts a lifecycle approach, covering both pre-deployment and usage phases, and that not all elements must be present simultaneously. India’s MeitY Governance Guidelines, while not adopting this definition verbatim, track its lifecycle orientation and the distinction between system objectives and the intended purpose set by the deployer.

OECD Recommendation on Artificial Intelligence (2019, updated 2023)

The OECD Recommendation, updated in November 2023, defines an AI system as:

“a machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”

This formulation removes the explicit ‘autonomy’ and ‘adaptiveness’ elements found in the EU AI Act and instead treats them as descriptive properties that differ across AI systems. The OECD definition has become the de facto international standard, explicitly or implicitly adopted by the EU AI Act, the US NIST AI Risk Management Framework, the ASEAN Guide on AI Governance, and the Council of Europe’s Framework Convention. The Government of India, in its India AI Governance Guidelines, refers to the OECD definition as the “global baseline” to which the Indian functional, lifecycle-based description is aligned, ensuring interoperability without fettering domestic policy flexibility.

UNESCO Recommendation on the Ethics of Artificial Intelligence (2021)

The UNESCO Recommendation, adopted by the General Conference in November 2021, defines an AI system as:

“information‑processing technologies that integrate models and algorithms that produce a capacity to learn and to perform cognitive tasks, leading to outcomes such as prediction and decision‑making in material and virtual environments.”

The UNESCO definition emphasises the cognitive character of AI tasks—learning and performing tasks normally requiring human intelligence rather than the technical architecture alone. It is embedded within a broader ethical framework that stresses human rights, diversity, inclusion, environmental flourishing, and the necessity of human oversight. India has actively participated in the Recommendation’s implementation, and its emphasis on proportionality, fairness, and human-centred design is reflected in the DEEP-MAX scorecard and the seven sutras of the MeitY Guidelines.

United States: Decentralised Federal Inventories as a De Facto High‑Risk Register

In the United States, there is no single national “high‑risk AI system” register. Instead, a decentralised, agency‑level disclosure mechanism has been built through two primary instruments:

Advancing American AI Act (2022)

Codified in the FY 2023 National Defense Authorization Act, this statute directs the Director of the Office of Management and Budget (OMB) to require each federal agency to establish and maintain a publicly accessible AI Use Case Inventory. The Inventory must identify every AI system the agency uses, the system’s purpose, development stage, data sources and sensitivity, the risk‑management practices applied, and the extent of human oversight. This creates a legislative mandate for transparency across the entire executive branch.

OMB Memorandum M‑24‑10 (March 2024)

This memorandum operationalises the Advancing American AI Act for civilian agencies. It requires agencies to categorise their AI use cases as either “rights‑impacting” or “safety‑impacting” categories that functionally map onto the OECD/EU definition of “high‑risk.” For any system so designated, the agency must conduct detailed risk assessments, document the results in the Inventory, and apply minimum risk‑management practices.

The combined effect is that the phrase “high‑risk AI system,” though not a statutory term in US law, is given concrete form. The aggregated, searchable inventories constitute the functional equivalent of a centralised high‑risk register, but one built from the bottom up, co‑ordinated by the OMB’s Chief Artificial Intelligence Officers Council.

AI System' in Indian Official Documents

This analysis examines how three foundational government reports released between November 2025 and January 2026 construct the concept of an "AI system" in India. While all three reports deliberately avoid a rigid statutory definition in favour of functional, operational descriptions, each adopts a distinct lens shaped by its institutional mandate—executive policy, judicial administration, and techno-legal governance, respectively.

Report: India AI Governance Guidelines (MeitY Drafting Committee Report, November 2025)

Document Overview

The Ministry of Electronics and Information Technology (MeitY) released the India AI Governance Guidelines on 5 November 2025, a 68-page document developed by a specially constituted Drafting Committee chaired by Prof. Balaraman Ravindran (IIT Madras) and comprising experts from government, academia, industry, and legal practice. The drafting process is reported to have involved approximately 2,500 public consultations, reflecting a deliberate effort to build consensus across a broad spectrum of stakeholders.

The Guidelines explicitly state that India will not enact a dedicated, omnibus AI statute—at least for the present. Instead, the country adopts a sectoral regulatory model under which existing statutory frameworks (the Information Technology Act, 2000; the Digital Personal Data Protection Act, 2023; the Bharatiya Nyaya Sanhita, 2023; and consumer protection legislation) are supplemented by sector-specific rules administered by regulators such as the RBI, SEBI, and IRDAI.

Definitional Approach: Functional, Lifecycle-Based, and Statutorily Open

The Guidelines expressly reject a rigid statutory definition of "AI system," a position grounded in the recognition that AI is a general-purpose, rapidly evolving technology whose boundaries cannot be captured in a single legislative formulation without risking either overbreadth or premature obsolescence. In lieu of a fixed definition, the Guidelines adopt a functional, lifecycle-based description encompassing three phases:

1. Development: The design, training, and testing of AI models, including data collection and pre-processing;
2. Deployment: The integration of AI systems into operational environments, including decisions about use cases and user-facing interfaces;
3. Post-Deployment Adaptation: Ongoing monitoring, retraining, fine-tuning, and decommissioning, recognizing that modern AI systems—particularly those employing continuous learning—evolve after initial release.

This lifecycle approach is significant because it determines the scope of regulatory obligations: an entity's responsibilities under the Guidelines are not tied to whether a given piece of software meets some abstract taxonomic threshold, but rather to the function that software performs at each stage of the value chain.

Risk Categorisation: India-Specific Categories in Lieu of the EU Pyramid

Where the EU AI Act organises regulatory obligations according to a four-tier risk pyramid (unacceptable, high, limited, and minimal risk), the Indian Guidelines adopt a categorisation grounded in distinctly Indian concerns:

India-Specific Risk Category	Description
Malicious Use	Weaponisation of AI for disinformation, cyber-attacks, fraud, and national security threats, including AI-enabled social engineering at scale
Discrimination Against Vulnerable Groups	Algorithmic bias affecting Scheduled Castes, Scheduled Tribes, Other Backward Classes, religious minorities, and linguistic communities, with particular attention to India's socio-economic diversity
Deepfakes and Synthetic Content	AI-generated media that impersonates individuals, fabricates evidence, or manipulates electoral discourse, identified as an acute threat given India's large digital population and active electoral cycle
Autonomous Loss of Control	Risks arising from AI systems operating without adequate human oversight in critical domains such as law enforcement, welfare disbursement, and healthcare

This taxonomy reflects a deliberate policy choice to anchor risk assessment in India's constitutional commitment to substantive equality (Article 14–15) and the specific vulnerabilities of its population, rather than adopting a risk framework developed primarily for mature European regulatory environments.

The Seven Sutras: India's Foundational AI Principles

The Guidelines articulate seven "Sutras" (core principles), adapted from the RBI's Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) published in August 2025.

Sutra	Core Mandate
Trust is the Foundation	Trust must be embedded across the entire AI value chain—technology, stakeholders, and users—as a precondition for adoption at scale
People First	Human-centric design, oversight, and empowerment; human-in-the-loop mechanisms at critical decision points
Innovation over Restraint	All things being equal, responsible innovation is prioritised over cautionary restraint, establishing a presumption in favour of deployment
Fairness and Equity	AI systems must be tested for bias, designed with culturally representative datasets, and ensure non-discriminatory outcomes for marginalised communities
Accountability	Clear attribution of liability proportionate to role and risk, enforced through policy, technical, and market-led mechanisms
Understandable by Design	AI systems must be explainable and interpretable; regulators and users should understand how the system works and its likely outcomes
Safety, Resilience & Sustainability	Systems must minimise harm, detect anomalies, and be environmentally responsible (lightweight models encouraged)

The third Sutra—"Innovation over Restraint"—is the most consequential doctrinal innovation and the one that most sharply distinguishes India's approach from the EU's pre-market, precautionary model. It establishes what the Internet Freedom Foundation has described as a "presumption in favour of deployment" when regulators face uncertainty.

Liability Across the Value Chain

The Guidelines introduce a graded liability system under which accountability is allocated proportionately across the AI value chain. Developers, deployers, and users each bear obligations commensurate with their role and the level of risk their activities create. This marks a departure from binary models that place liability exclusively on either the developer or the end-user.

Institutional Architecture

The Guidelines propose a multi-tier institutional framework:

• AI Governance Group (AIGG): Chaired by the Principal Scientific Adviser, this group coordinates across ministries and regulators to establish uniform standards and address regulatory fragmentation.
• AI Safety Institute (AISI): The primary centre for evaluating, testing, and researching the safety of AI systems across sectors, supporting the IndiaAI Mission by developing techno-legal tools for content authentication, bias detection, and cybersecurity.
• National AI Incident Database: A centralised repository for recording and analysing AI-related safety failures, biased outcomes, and security breaches, designed to inform evidence-based regulatory interventions.

Techno-Legal Measures in Lieu of Bans

The Guidelines steer away from outright prohibitions, instead promoting a suite of techno-legal tools: watermarking and content provenance mechanisms (aligned with global standards such as C2PA), bias audits, explainability tools, transparency reports, model cards, and red-teaming exercises.

Implementation Roadmap (2026–2028)

The Guidelines include a phased, time-bound action plan:

• Short-term (2026): Sectoral regulators to issue AI-specific circulars; establishment of the AIGG and TPEC
• Medium-term (2027): Operationalisation of the AISI and National AI Incident Database; development of India-specific risk assessment methodologies
• Long-term (2028 and beyond): Judges' pilot schemes for AI-assisted legal research and case management; integration of AI governance into Digital Public Infrastructure (DPI); harmonisation with international standards

Report B: White Paper on Artificial Intelligence and Judiciary (Supreme Court CRP, November 2025)

Document Overview

The White Paper on Artificial Intelligence and Judiciary was released in November 2025 by the Centre for Research and Planning (CRP) of the Supreme Court of India. It represents the first official, comprehensive articulation by the Indian judiciary of how AI should be understood, deployed, and regulated within the justice system. The document traces the global trajectory of AI in courts—from basic digitisation to advanced machine-learning tools—and situates India's own journey within this broader landscape.

Definition of AI System: A Suite of Cognitive Capabilities

The White Paper defines an "AI system" as:

"Machine systems that are able to do tasks usually needing human intellect, such as reasoning, pattern recognition, understanding language, and making decisions in a structured way."

This formulation is notable for several reasons. First, it anchors the definition in cognitive function—reasoning, pattern recognition, language understanding, structured decision-making—rather than in technical architecture (e.g., neural networks, transformers). Second, by enumerating specific capabilities rather than relying on a single abstraction (such as "inferencing"), the definition is tailored to judicial use cases: each capability maps directly onto a task that judges, registrars, and lawyers perform. Third, the definition implicitly distinguishes assistive systems (tools that inform judicial reasoning) from determinative systems (tools that replace it), with the White Paper consistently emphasising the former and prohibiting the latter in core judicial functions.

Active AI Tools in the Indian Judiciary: SUPACE, SUVAS, and Beyond

The White Paper documents the current state of AI deployment within the Indian judiciary, cataloguing tools already in operation or advanced experimental stages:

Tool	Full Name	Function
SUPACE	Supreme Court Portal for Assistance in Courts Efficiency	AI-assisted fact extraction, legal provision identification, and intelligent search of precedents to provide judges with relevant material for ongoing cases; remains in experimental stage
SUVAS	Supreme Court Vidhik Anuvaad Software	Machine translation of judgments; has translated over 36,271 Supreme Court judgments into Hindi and 17,142 judgments into 16 regional languages (as of 2024), with the original English judgment remaining legally authoritative
TERES	AI-Based Transcription	Real-time transcription of court proceedings
LegRAA	Legal Research Analysis Assistant	AI-powered legal research and analysis

These tools are expressly categorised as aids to judicial function, not substitutes for judicial reasoning. The White Paper is emphatic on this point: "Speed cannot come at the cost of justice," and "judges must remain the final authority at all times."

Ethical Principles: Six Pillars for Judicial AI

The White Paper articulates six firm principles governing any AI system deployed in the judiciary:

1. Human in the Loop: No AI system may make a final judicial determination; human judges must retain ultimate decisional authority
2. Accuracy & Verification: AI-generated outputs must be independently verified; reliance on unverified AI content (including AI-"hallucinated" citations) is impermissible
3. Confidentiality & Privacy: AI systems must not store, transmit, or expose confidential case data; free, public AI tools are identified as a particular risk
4. Bias Prevention: Systems must be tested for algorithmic bias, with particular attention to racial, caste-based, and socio-economic discrimination
5. Clear Disclosure: Users must disclose when AI has been used in the preparation of filings; courts must disclose when AI tools have been employed in case processing
6. Restricted Roles for AI in Core Judicial Functions: AI may assist with administrative and preparatory tasks (summarisation, translation, case categorisation) but must not be used for substantive adjudication

Institutional Safeguards

The White Paper recommends concrete structural reforms: AI Ethics Committees within every court (comprising technical and legal experts), mandatory training for judges, clerks, and registry staff on AI capabilities and risks, disclosure norms requiring parties to state when generative AI has been used in preparing filings, and a strong preference for secure, in-house AI systems over commercial, cloud-based models.

Training Data Ownership and Judicial Control

The White Paper addresses the question of training data ownership—a critical issue for judicial AI systems, which must operate on curated, authoritative legal datasets. It recommends that courts retain ownership and control over the datasets used to train judicial AI tools, ensuring that the underlying legal materials are complete, unbiased, and jurisdictionally accurate. This is linked to the broader recommendation that judicial AI be developed in-house rather than procured from commercial vendors whose training corpora may be opaque or contain extraneous, unreliable content.

Report C: PSA White Paper on Strengthening AI Governance Through Techno-Legal Framework (January 2026)

Document Overview

The Office of the Principal Scientific Adviser (PSA) to the Government of India released the White Paper titled Strengthening AI Governance Through Techno-Legal Framework on 23 January 2026. This 40-page document is the second in a series on "Emerging Policy Priorities for India's AI Ecosystem" (the first, released in December 2025, focused on democratising access to AI infrastructure). It reinforces, extends, and operationalises the governance architecture outlined in the MeitY Guidelines.

Definitional Approach: Reinforcing the Functional Paradigm

The PSA White Paper does not introduce a new statutory definition. Instead, it explicitly endorses the MeitY Guidelines' functional, lifecycle-based approach and confirms India's policy choice not to legally define "AI system" in a rigid statutory form. The rationale, as articulated across both documents, is two fold:

1. Technological Neutrality: A fixed definition may inadvertently exclude future AI paradigms (e.g., neuromorphic computing, biological computing) or capture technologies not intended for regulation.
2. Policy Flexibility: Avoiding legislative entrenchment allows India to adapt its governance posture as the technology evolves without undertaking protracted legislative amendment processes.

The Techno-Legal Model: Embedding Governance by Design

The White Paper's central contribution is the articulation of a "techno-legal" framework—defined as the integration of legal instruments, rule-based conditioning, regulatory oversight, and technical enforcement mechanisms directly into the architecture of AI systems by design. This model rests on four founding pillars:

Pillar	Description
Standardised Automated Checks	Technical controls embedded at the system level to enforce compliance automatically
Measurable Accountability Through Audit Trails	Immutable logs of AI system operations enabling post-hoc review and liability attribution
Low-Cost Compliance Using Public Infrastructure	Leveraging India's Digital Public Infrastructure (DPI) to reduce the compliance burden on smaller entities
Future-Readiness	A flexible architecture capable of accommodating new regulatory requirements without fundamental redesign

Five Lifecycle Stages of Governance

The White Paper maps governance mechanisms onto five stages of the AI lifecycle, expanding on the MeitY Guidelines' three-phase model:

1. Data Collection
2. Data Protection
3. Model Training
4. Safe Inference
5. Trusted Agents (post-deployment monitoring and decommissioning)

Institutional Mechanisms Reaffirmed and Extended

The White Paper reaffirms the institutional architecture proposed in the MeitY Guidelines and adds operational detail:

• AI Governance Group (AIGG): Chaired by the PSA, responsible for inter-ministerial coordination and uniform standard-setting.

• Technology and Policy Expert Committee (TPEC): Housed within MeitY, comprising multidisciplinary expertise spanning law, public policy, machine learning, AI safety, and cybersecurity. TPEC advises the AIGG on issues of national importance.
• AI Safety Institute (AISI): The primary centre for evaluation, testing, and safety research; responsible for developing techno-legal tools for content authentication, bias detection, and cybersecurity.
• National AI Incident Database: A centralised mechanism for recording, classifying, and analysing AI-related safety failures, biased outcomes, and security breaches, drawing on global best practices such as the OECD AI Incident Monitor but adapted to India's sectoral realities.

AI Subject Protection and Digital Public Infrastructure

The White Paper draws a critical distinction between "AI users" (those who actively interact with AI systems) and "AI subjects" (those who are passively affected by automated decisions). It advocates for specific protections for AI subjects, particularly in the context of welfare disbursement, where algorithmic decisions may affect access to essential benefits without the subject's knowledge or consent.

The White Paper also proposes leveraging India's existing Digital Public Infrastructure (DPI) to enable consent-based data access and automated auditability, reducing compliance costs and ensuring that AI governance is not limited to well-resourced entities.

Deepfake Mitigation and Privacy-Enhancing Technologies

Beyond content takedown mechanisms, the PSA White Paper advocates for content provenance and cryptographic metadata as systemic solutions to the deepfake challenge. It promotes privacy-enhancing technologies (PETs) such as differential privacy and homomorphic encryption to enable model training without exposing sensitive personal information, while acknowledging that privacy safeguards can sometimes impact model utility or inclusivity, necessitating "impact-aware" mechanisms.

Relationship to the MeitY Guidelines

The PSA White Paper is explicitly positioned as a complement to not a replacement for the MeitY Guidelines. Where the Guidelines establish the principles (the "what" and "why"), the PSA White Paper provides the institutional and technical machinery (the "how"). Together, the two documents constitute the most comprehensive articulation of India's AI governance framework to date.

Synthesis: The Emerging Indian Conception of "AI System"

Taken together, these three reports reveal a coherent though deliberately non-codified conception of "AI system" in Indian official thought, characterised by five interlocking features:

1. A Rejection of Rigidity: India has made a deliberate, consensus-driven policy choice not to embed a fixed statutory definition of "AI system" in law. This decision is not a failure of legislative imagination but a strategic response to the pace of technological change and the breadth of AI's applications across sectors.
2. A Functional, Lifecycle-Based Approach: In lieu of a definition, India adopts a functional description anchored in the full AI lifecycle—development, deployment, and post-deployment adaptation—with regulatory obligations attaching to what an AI system does rather than what it is.
3. India-Specific Risk Categorisation: Risk is categorised by reference to constitutional values and India's specific socio-economic vulnerabilities—malicious use, discrimination against vulnerable groups, deepfakes, and autonomous loss of control—rather than the abstract tiers of the EU's risk pyramid.
4. Techno-Legal Governance by Design: The PSA White Paper's "techno-legal" model embeds legal safeguards directly into AI system architecture, reflecting a preference for ex ante technical measures (watermarking, bias audits, explainability tools) over ex post punitive sanctions or outright bans.
5. Differentiated Institutional Lenses: The three reports illuminate different facets of the same underlying conception: the MeitY Guidelines provide the governance architecture; the Supreme Court White Paper tailors the framework to the unique requirements of the judiciary (human-in-the-loop, in-house tools, judicial data ownership); and the PSA White Paper supplies the technical and institutional machinery for operationalisation.

Types of AI Systems

Globally, AI systems are classified along several axes: by capability (narrow, general, superintelligence), by cognitive function (discriminative, generative), and by interaction style (conversational agents, recommender systems, autonomous systems, decision‑support tools). As of March 2026, no Indian statute prescribes a mandatory classification. The India AI Governance Guidelines (MeitY, November 2025) adopt a light‑touch, principles‑based, innovation‑friendly posture that promotes voluntary compliance, contextual risk assessment, and the avoidance of prescriptive taxonomies that could freeze fast‑evolving technologies. However, official policy discourse, shaped by NITI Aayog’s legacy frameworks and the OECD/UNESCO baseline, clusters AI systems into the overlapping categories set out below.

Classification by Capability Level (NITI Aayog Legacy Framework)

This taxonomy, rooted in the 2018 National Strategy for AI and refined through 2025–2026, remains the primary capability‑referencing tool in Indian policy documents.

• Narrow AI (ANI / Weak AI) – Outperforms humans on a specific, well‑defined task. Every operational system in India falls into this category. Examples include image‑recognition models, voice assistants (e.g., Alexa, Google Assistant), recommender engines (Netflix, YouTube), and judicial aids such as SUPACE (case summarisation) and SUVAS (translation).
• Artificial General Intelligence (AGI / Strong AI) – Hypothetical systems possessing at least human‑level intelligence across a broad range of intellectual tasks without task‑specific retraining. Not yet realised. Research laboratories funded by the IndiaAI Mission are developing adaptive multilingual models that approach this horizon.
• Artificial Superintelligence (ASI) – Surpasses human intelligence in every domain. Neither deployed nor expected in the short term. The MeitY Guidelines identify ASI as a potential “loss of control” risk that warrants continuous monitoring.

Classification by Processing Style

This functional categorisation, drawn from standard AI handbooks and cited in the MeitY Guidelines, distinguishes systems by how they handle memory and reasoning:

• Reactive Machines – No memory; decisions are based solely on current inputs (e.g., early chess engines, basic fraud detectors).
• Limited Memory – Reference past experiences to inform present decisions. Most contemporary AI systems—from self‑driving car prototypes to predictive‑policing tools—are limited‑memory architectures.
• Theory of Mind – (Hypothetical) Ability to understand the emotions, beliefs, and intentions of others.
• Self‑Aware AI – (Hypothetical) Possesses consciousness and self‑awareness.

Classification by Core Technology and Modern Applications (Dominant in 2025–2026 Indian Discourse)

The 2025–2026 policy conversation in India revolves around the following six categories, which reflect the immediate opportunities and risks of the current AI generation:

1. Discriminative AI – Focuses on categorisation and pattern recognition. Applications include fraud‑detection engines, spam filters, and automated court‑document classification.
2. Generative AI – Creates new content (text, images, code, audio). The most commonly encountered AI type among Indian users, accessed through chatbots (ChatGPT, Gemini) and legal‑drafting assistants. The 2026 amendments to the IT (Intermediary Guidelines) Rules impose a mandatory labelling requirement for all synthetically generated or modified content.
3. Agentic AI – Autonomous agents that plan, reason, and execute multi‑step tasks using external tools. Identified as a high‑potential, high‑risk category; agentic systems can amplify “loss of control” scenarios if safeguards are weak.
4. Multimodal AI – Processes multiple data formats (text, image, audio, video) simultaneously. Indian‑language multimodal models are under active development under the Bhashini mission but still lag behind English‑centric counterparts.
5. Predictive AI – Forecasts outcomes based on historical patterns. Pilot projects in courts—such as case‑pendency‑prediction tools—are being tested under strict human‑in‑the‑loop protocols.
6. Conversational AI – Natural‑language interfaces designed for human‑like dialogue. Judicial chatbots (integrated into SUPACE) and citizen‑facing grievance‑redressal bots are the most visible public‑sector deployments.

India‑Specific High‑Risk AI Classification

India deliberately avoids the EU’s four‑tier risk pyramid (unacceptable, high, limited, minimal). Instead, the MeitY Guidelines and subsequent PSA White Paper urge stakeholders to assess risk through an India‑contextualized lens that prioritizes:

• National‑security threats (cyber‑attacks, disinformation campaigns);
• Discrimination and harm to vulnerable groups: deepfakes targeting women, child‑safety risks, caste‑based algorithmic profiling, and linguistic exclusion;
• *Bias in 22+ official languages* and hundreds of dialects, where standardized legal terminology is often absent;
• Caste‑based discrimination encoded in training data, which scholars and parliamentary committees have flagged as a unique Indian risk vector.
• The Guidelines identify general risk categories—malicious use, algorithmic discrimination, lack of transparency, systemic risk, loss of control, threats to national security—but explicitly decline to create a fixed taxonomy. Instead, they call for an industry‑led, multiple‑accountability‑model that interleaves voluntary compliance with sector‑specific regulatory enforcement. This approach mirrors the “Safe & Trusted AI” sutras: Trust, People First, Fairness & Equity, Accountability, Understandable by Design, Safety/Resilience/Sustainability, and Innovation over Restraint.

Regional and Contextual Variations: Perspectives from Legal Practitioners and Policy Analysts

Legal experts—including Prashant Mali, Ronin Legal Consulting, Bar Council committees, and the Supreme Court e‑Committee—stress that India’s classification must absorb four ground realities:

• Linguistic diversity. AI systems must serve all 22 official languages and major vernacular dialects. SUVAS’s custom language classification directly addresses access‑to‑justice imperatives, but the absence of standardised legal terminology in regional languages creates unique bias and accuracy risks.
• Federal diversity. Central principles under MeitY and IndiaAI are implemented differently by High Courts and states. Kerala and Odisha, for instance, deploy proprietary internal AI tools, leading to uneven developer/deployer liability standards.
• Judicial AI as strictly assistive technology. The 2025 Supreme Court White Paper mandates that AI systems may never act as judicial decision‑makers. SUPACE (research/summarisation), SUVAS (translation), TERES (transcription), and LegRAA (metadata extraction) are all classified as assistive. Any predictive or risk‑scoring tool (e.g., recidivism prediction) is treated as high‑risk/sensitive, requiring a pilot study and a formal ethics assessment.
• Vulnerable‑group focus. Researchers from DAKSH and Digital Futures Lab recommend that India centre its classification on caste, language, and gender discrimination, with EU‑style tier systems serving as a secondary reference. The Private Member’s AI (Ethics and Accountability) Bill, 2025, proposes statutory ethics committees with the power to audit high‑impact systems.

In sum, India’s AI‑system classification is practical, lifecycle‑oriented, and harm‑based. It borrows OECD capability/functionality schemas but filters risk through India’s constitutional requirement of substantive equality (Articles 14–15). It operates not through outright prohibition but through a techno‑legal governance model that embeds safety and fairness tools (watermarking, bias audits, explainability requirements) into the very architecture of AI systems.

APPEARANCE IN THE OFFICIAL DATABASE

The term "AI system" appears across a constellation of official databases, repositories, and compliance registries in India, though not in a single, unified, mandatory pre-market register. As of May 2026, India does not maintain a centralized, statutory registry of AI systems comparable to the EU AI Act’s Article 71 EU Database for high-risk AI systems. Instead, India relies on a distributed accountability architecture comprising:

• Project repositories and policy databases maintained by the India AI Mission and the e-Courts project, which document AI tools as they are developed, deployed, and assessed;
• Statutory compliance records mandated under the Digital Personal Data Protection Act, 2023, which apply to AI systems that process personal data at scale;
• Judicial data platforms such as the National Judicial Data Grid (NJDG), which supplies the raw material for AI analysis of court performance;
• Proposed forward-looking registries—the National AI Incidents Database and the AIKosh Dataset Platform—which, though not yet operational, are embedded in official policy documents.

This section maps each database, identifies the apex departments and agencies responsible for data creation and curation, describes the methods used to collate and present judicial data, and situates India’s approach within a comparative international context.

Apex Departments and Agencies: Who Creates and Curates Judicial AI Data?

The Indian judicial AI ecosystem is governed by a layered institutional architecture in which data creation, collation, and presentation are dispersed across multiple agencies, each with a distinct mandate. The following table identifies the principal actors:

Agency / Department	Mandate	Key AI-Related Databases Maintained
e-Committee, Supreme Court of India	Policy, strategy, and oversight for the e-Courts Mission Mode Project	NJDG, e-Courts Phase III technical documentation, AI tool specifications (SUPACE, SUVAS, LegRAA, TERES, Digital Courts 2.1), Supreme Court CRP White Paper
Ministry of Electronics and Information Technology (MeitY)	Formulates AI governance policy; houses IndiaAI Mission	IndiaAI.gov.in repository, AI Governance Guidelines (November 2025), proposed National AI Incidents Database
National Informatics Centre (NIC)	Technical implementation and software development	NJDG back-end, ICMIS (Integrated Case Management & Information System), e-Courts Phase III platforms, AI model prototyping
Department of Justice (DoJ), Ministry of Law & Justice	Funding, coordination, and parliamentary reporting for e-Courts	Detailed Project Report (DPR) for e-Courts Phase III, budget allocations for Future Technological Advancements, parliamentary question replies on AI use
Office of the Principal Scientific Adviser (PSA)	Techno-legal coordination and whole-of-government AI governance	PSA White Paper (January 2026), proposed AI Governance Group (AIGG), Technology & Policy Expert Committee (TPEC)
IndiaAI Mission (under MeitY)	Pillar-wise implementation of India’s AI strategy	AIKosh Dataset Platform, IndiaAI Mission repository, project proposals and pilot reports
Supreme Court Centre for Research and Planning (CRP)	Legal and interdisciplinary research on judicial reform	White Paper on Artificial Intelligence and Judiciary (November 2025), research series on court efficiency

Principal Databases and Registers Where ‘AI System’ Appears

The National Judicial Data Grid (NJDG)

The NJDG is a flagship project implemented under the aegis of the e-Committee, Supreme Court of India. It is a national repository of case statistics that provides a comprehensive database of orders, judgments, and case details from District and Subordinate Courts and High Courts. Statistical data is updated automatically on a daily basis and is accessible to the public.

Although the NJDG is not itself an AI system registry, it is the foundational data source from which AI tools in the judiciary operate. The Supreme Court’s White Paper on AI and the Judiciary (November 2025) makes this linkage explicit: by analyzing NJDG data at scale, AI systems can identify systemic bottlenecks, evaluate the impact of procedural reforms, and support evidence-based decision-making by High Courts and the Supreme Court in their administrative capacities. The NJDG thus constitutes the data substrate for any AI application that seeks to predict case pendency, classify case types, or analyze judicial trends—making it a de facto component of the AI data ecosystem even though “AI system” is not listed as a category within the grid itself.

India AI Mission Repository

The portal indiaai.gov.in operates as the Government of India’s central AI information hub. It hosts case studies, project descriptions, and policy papers that collectively define AI systems as assistive tools for the judiciary. Key entries on judicial AI include:

• SUPACE (Supreme Court Portal for Assistance in Courts Efficiency): An AI-enabled assistive tool that augments the efficiency of legal researchers and judges by extracting facts, identifying legal provisions, performing intelligent searches for precedents, and drafting case documents. The India AI portal describes SUPACE as a “perfect blend of human intelligence and machine learning” that adapts to user behavior through incremental usage.
• SUVAS (Supreme Court Vidhik Anuvaad Software): A machine-learning translation tool used to translate Supreme Court judgments from English into vernacular languages. As of 2024, over 36,271 judgments had been translated into Hindi and 17,142 into 16 regional languages.
• AI in Judicial Processes (February 2025): A comprehensive article detailing AI-driven enhancements under e-Courts Phase III, including intelligent scheduling, automated filing, NLP-based legal research and translation, AI-powered chatbots for litigant assistance, and data security protocols.

The repository does not adopt a rigid classification schema but instead presents each tool through a functional description—what the system does, for whom, under what safeguards—thereby embedding an operational definition of “AI system” as any software that aids, but does not replace, human judicial decision-making.

E-Courts Phase III Technical Documentation and NIC Project Reports

The e-Courts Phase III project, with a financial outlay of ₹7,210 crore, includes a dedicated component for Future Technological Advancements (₹53.57 crore) that explicitly covers AI and blockchain. Technical documentation and project reports housed by the National Informatics Centre (NIC) and the Department of Justice describe AI tools as follows:

• Legal Research Analysis Assistant (Leg RAA): An AI-based tool developed to aid judges in legal research and document analysis. According to the Minister of State for Law and Justice’s written reply to the Lok Sabha on 13 March 2026, Leg RAA “take[s] care of the issues of data privacy and ethical safeguards by using Court’s own data i.e. judgments and orders passed by the Supreme Court, High Courts and District Courts.”
• Digital Courts 2.1: A platform that assists Judges and Judicial Officers in managing courts in a paperless manner by providing a single window for all case-related information. It integrates voice-to-text (ASR-SHRUTI) and translation (PANINI) functionalities.
• TERES (Transcription): AI-based real-time transcription of Supreme Court Constitution Bench hearings.
• AI prototypes developed with IIT Madras: Tools for curing document defects, metadata extraction, and integration with the electronic filing module and the Integrated Case Management & Information System (ICMIS).

Crucially, all these tools are developed in-house, using only court-owned data, and operate under strict human oversight. The Detailed Project Report (DPR) for e-Courts Phase III mandates that AI tools be subject to controlled pilot deployments before any broader rollout, with operational frameworks governed by the rules of business and policies of the respective High Courts.

DPDP Act Compliance Records (De Facto AI Register)

The Digital Personal Data Protection Act, 2023 (DPDP Act), while not an AI-specific statute, creates a compliance paper trail that functions as a de facto register of high-impact AI systems. Sections 8–11 of the Act classify large-scale data processors as Significant Data Fiduciaries (SDFs) and impose the following obligations, each of which generates auditable documentation relevant to AI:

• Data Protection Impact Assessment (DPIA) (Section 10): An SDF must conduct a DPIA prior to undertaking any processing that poses a high risk to data principals’ rights. For AI systems that process case data—such as SUPACE or LegRAA—the DPIA examines risks of re-identification, biased outputs, and adverse impacts on the right to a fair hearing. This is functionally equivalent to an algorithmic impact assessment, even though the DPDP Act does not use that terminology.
• Independent Data Audits (Section 11): SDFs must undergo periodic independent audits, which in the AI context translate into evaluations of model fairness, accuracy, robustness, and explain ability.
• Appointment of a Data Protection Officer (Section 9): An SDF must appoint a DPO based in India who serves as the point of contact for grievances, including those arising from algorithmic profiling.

These records—DPIAs, audit reports, and DPO designations—constitute a statutorily mandated documentation trail for any AI system that processes personal data at scale. While not publicly accessible in the manner of the EU High-Risk AI Database, the records are subject to oversight by the Data Protection Board of India and available to regulators upon demand.

National AI Incidents Database (Proposed)

Both the MeitY AI Governance Guidelines (November 2025) and the PSA White Paper (January 2026) recommend the establishment of a National AI Incidents Database. The PSA White Paper proposes a national registry that would log, classify, and analyse AI risks and incidents, capturing reports from public bodies, private companies, researchers, and civil society. The database would focus on:

• Safety failures
• Biased outcomes
• Security breaches and lapses
• Misuse (including deepfakes and AI-enabled disinformation)

The PSA proposal explicitly recommends aligning the database with OECD definitions of AI incidents to ensure international comparability and enable India’s participation in cross-border incident-sharing arrangements. The India AI Governance Guidelines further elaborate that the database would feed into an India-specific risk taxonomy, facilitate data-driven audits, and enable the detection of systemic trends and emerging threats.

AI Kosh: The India AI Dataset Platform

Launched on 6 March 2025 under the India AI Mission with an allocation of ₹199.55 crore, AIKosh is a unified platform integrating datasets from diverse sources to serve as a national repository of India-specific anonymous and non-personal datasets, models, and use cases. As of late 2025, it hosted over 300 datasets and 80 models across 13 sectors from 12 organizations, including government institutions such as the Indian Council of Medical Research and Bhashini.

AI Kosh’s role in the database landscape is threefold:

• Data curation: Datasets are structured, labelled, and annotated specifically for AI/ML training, with robust access controls (open, restricted, and private datasets).
• Bias auditing: The pipeline envisions subjecting datasets to fairness audits before their release for AI training, addressing concerns of caste, language, and gender bias.
• Court-owned repositories: The platform aligns with the Supreme Court CRP White Paper’s recommendation that training data for judicial AI be curated in-house, under court ownership, to prevent contamination by commercial or extraneous corpora.

Supreme Court CRP White Paper (November 2025) as an Official Reference Document

Although not a database in the technical sense, the Supreme Court’s White Paper on Artificial Intelligence and Judiciary (November 2025) functions as an authoritative registry of active judicial AI tools. It catalogues every AI system deployed under the Court’s auspices, maps them against international benchmarks, and documents ethical risks and mitigation measures. The White Paper is an official publication of the Supreme Court’s Centre for Research and Planning and constitutes the most comprehensive single record of AI systems in the Indian judiciary.

Research That Engages with “AI System” in the Indian Judiciary

Research on AI systems in Indian courts has expanded rapidly since 2021–2022, accelerating markedly in 2025–2026 as the e‑Courts Phase III project moved from procurement to live deployment. The published corpus spans academic law journals, computer‑science conference proceedings, policy think‑tank reports, official white papers, and parliamentary documentation. The dominant investigative themes are threefold: (a) the potential of AI to reduce India’s backlog of over 50 million pending cases; (b) improving access to justice—especially for speakers of India’s 22 official languages and numerous vernacular dialects; and (c) identifying and mitigating the ethical, legal, and technical risks that accompany algorithmic decision‑making in the justice system. Compared with official documents, which adopt a cautiously affirmative tone, the research literature consistently moves beyond efficiency narratives to interrogate constitutional compliance, social equity, and institutional accountability.

Key Research Clusters (2024–2026)

Cluster 1: Evaluation of Active Judicial AI Tools

The first corpus of work assesses the design, performance, and field impact of four tools developed or commissioned by the e‑Committee, Supreme Court of India: SUPACE (legal research and case summarisation), SUVAS (judgment translation into 16+ Indian languages), LegRAA (document analysis and metadata extraction), and TERES / Adalat AI (real‑time transcription of court hearings).

– The Supreme Court Centre for Research and Planning’s White Paper on Artificial Intelligence and Judiciary (November 2025) is the most authoritative single document in this cluster. It catalogues each tool, benchmarks them against analogous systems in the United States, China, and the European Union, and systematically analyses risks—particularly hallucination (the generation of fictitious case citations) and dataset bias—while proposing human‑in‑the‑loop safeguards, mandatory algorithmic audits, and the creation of a judicial AI ethics committee.

– Case studies published on the IndiaAI portal (indiaai.gov.in) and in Press Information Bureau (PIB) releases (2025–2026) provide implementation‑level data, reporting reductions in legal‑research time of up to 30–40% in pilot courts and a sharp increase in the availability of vernacular‑language judgments.

– Academic contributions, including papers in the Indian Journal of Law and Social Innovation (2024) and Kurukshetra University Educational Youth Journal (2024), have modelled the potential case‑disposal gains from automating routine clerical and indexing tasks, suggesting that even a 10% efficiency gain could meaningfully dent the backlog over a five‑year horizon.

Cluster 2: Rights‑Based and Ethical Critique

A second, more critical strand of research places constitutional rights—especially Article 14 (equality before the law) and Article 21 (the right to a fair and speedy trial)—at the centre of the inquiry.

– The UNDP–DAKSH–Digital Futures Lab report, AI for Justice: Ethical, Fair, and Robust Adoption in India’s Courts (February 2026), is the landmark study in this cluster. Drawing on interviews with 60 judges, lawyers, and technologists across six states, the report documents widespread unofficial and unregulated use of free, consumer‑grade generative‑AI tools (such as ChatGPT) by lawyers and judges, significant gaps in judicial AI governance, concrete instances of bias and privacy violation, and the absence of any standardised pre‑deployment impact assessment. It proposes a practical, five‑stage evaluation framework—covering institutional readiness, rights‑impact by use case, technical benchmarking, operational integration, and ongoing monitoring—explicitly tailored to Indian courts.

– The Hindu Centre for Politics and Public Policy’s Policy Watch series (2024–2025 updates) and papers in Taylor & Francis journals (2026) have examined algorithmic accountability, transparency obligations under the Right to Information Act, 2005, and the tension between proprietary AI models and the open‑justice principle.

Cluster 3: Risk and Bias‑Focused Analysis

A significant body of work has concentrated on the specific risks of caste, gender, and language bias in training datasets, the phenomenon of AI hallucination producing fictitious legal citations, algorithmic opacity, and the difficulty of rendering machine‑generated advice explainable to unsophisticated litigants.

– Research published in the International Journal for Research in Applied Science and Engineering Technology (IJRASET, 2025), the practitioner‑oriented Virtuosity Legal platform (2025), and multiple 2026 law‑review articles warn that these failures can systematically deepen existing inequalities. The IJRASET study, for example, demonstrated that a commercially available legal‑language model, when queried on Indian family‑law scenarios, produced materially different recommendations depending on whether the litigant was identified by a caste‑associated surname.

– The Supreme Court CRP White Paper records at least two documented instances in 2024–2025 in which trial‑court judges relied on AI‑generated, non‑existent precedents, resulting in the initiation of disciplinary proceedings.

Cluster 4: Comparative and Future‑Oriented Perspectives

A growing body of comparative scholarship situates India’s “AI‑as‑assistive‑tool” model against the more autonomous judicial‑AI experiments underway in China (Smart Courts), the United States (risk‑assessment instruments in sentencing), and Estonia (AI‑driven small‑claims adjudication).

– Studies published in 2024–2026 consistently recommend a gradual, rights‑first adoption path for India, emphasising that India’s linguistic diversity, layered federal court structure, and constitutional commitment to substantive equality make wholesale importation of foreign models hazardous. Instead, scholars advocate for proactive, India‑specific regulation—enacted before AI systems scale beyond the current pilot phase—that mandates explainability, bias auditing, and meaningful human oversight.

Gaps and Overlaps in the Research

Several structural gaps and overlaps emerge from this body of work:

• Methodological Gaps: Evaluations of judicial AI tools remain predominantly qualitative, relying on self‑reported user satisfaction rather than rigorous, counterfactual‑based impact measurement. There is, as yet, no published study with hard data on how AI deployment has affected case‑disposal rates, litigation costs, or appeal‑to‑affirmance ratios.
• Temporal Overlap with Policy: Much of the academic output post‑2025 is reactive to, or directly commissioned by, the MeitY Guidelines and the Supreme Court White Paper. This creates a risk of “policy echo‑chamber” effects, where independent critique is tempered by institutional proximity.
• Geographical Coverage: Empirical work is heavily concentrated in the Supreme Court and a few High‑Court jurisdictions (Delhi, Bombay, Karnataka). There is a near‑total absence of field studies from district‑level courts, where 80% of India’s case backlog resides and where digital infrastructure is thinnest.
• Technical‑Legal Translation Gap: Research produced by computer scientists rarely engages with constitutional law doctrine (e.g., the reasonableness standard under Article 14), while legal scholarship often lacks the technical granularity needed to assess, for instance, the fairness metrics used in a model. The DAKSH–UNDP report is a rare bridging effort.

How Research Goes Beyond Official Documentation

Official sources—including the India AI Guidelines (2025), e‑Courts Phase III vision documents, and Supreme Court PIB releases—overwhelmingly frame AI as a safe efficiency tool under strict human control, emphasising implementation benefits and the broad principles of safety and trust.

Research, by contrast, goes further in four critical ways:

1. Constitutional Embedding: It ties every AI use case directly to fundamental‑rights obligations under Articles 14 and 21, warning that opaque, untested systems risk structural due‑process violations that efficiency gains alone cannot justify.
2. Documentation of Actual Harm: It records and analyses real‑world failures—such as the use of fake AI‑generated citations—that official documents acknowledge only in the abstract, thereby strengthening the empirical case for binding safeguards.
3. Mandatory, Not Voluntary, Governance: While official guidelines promote voluntary compliance, research overwhelmingly concludes that mandatory measures—explainable AI by design, independent pre‑deployment audits, statutory ethics committees, and a central incident database—are indispensable for protecting litigant rights.
4. Social‑Reality Integration: The research literature insists that India’s caste, linguistic, and digital‑divide realities must be treated as first‑order design constraints, not afterthoughts, demanding localised training datasets, dialect‑aware NLP, and indigenous knowledge integration that global‑benchmark‑driven official reports often underweight.

In sum, the 2025–2026 research on AI systems in the Indian judiciary constitutes a maturing field of inquiry, moving from technology‑application papers to rights‑grounded, empirically informed, institutionally aware critique. The emerging consensus is that proactive, India‑specific regulation—enacted at scale and enforced through an independent oversight architecture—is the necessary condition for harnessing AI’s efficiency potential without eroding the constitutional promise of equal justice.

Data Challenges

The evaluation, auditing, and evidence‑based regulation of AI tools in India’s judiciary face entrenched, multi‑dimensional data challenges. These are identified consistently across the Supreme Court White Paper (November 2025), the DAKSH–UNDP–Digital Futures Lab report (February 2026), and academic scholarship (2025–2026), and may be grouped under five heads.

Poor Data Quality, Incompleteness, and Inconsistency

Digitised court records from e‑Courts Phases I–III contain scanning and optical‑character‑recognition (OCR) errors, particularly acute for regional‑language documents written in non‑Latin scripts. Critical metadata—such as the caste, gender, or location of litigants—is often missing and fields across databases use inconsistent formats. The resulting “garbage‑in, garbage‑out” problem reduces model accuracy, introduces systemic noise into translation and precedent‑retrieval systems, and perpetuates the very inequalities the tools are intended to address.

Fragmented, Non‑Interoperable Systems

Judicial data is dispersed across silos: the National Judicial Data Grid (NJDG) for subordinate courts, the Integrated Case Management Information System (ICMIS) for the Supreme Court, and heterogeneous High‑Court‑specific portals. No uniform data schema enables cross‑court or inter‑state analysis. Rural and lower courts—which constitute the vast majority of India’s judiciary—lag significantly in digitisation, creating a “data desert” that renders AI tools trained on urban‑court data unreliable when deployed elsewhere.

Privacy, Confidentiality, and Security Constraints

Court records are replete with sensitive personal information of litigants, witnesses, and victims. The DPDP Act, 2023 imposes stringent anonymisation, purpose‑limitation, and security obligations. Yet, the DAKSH–UNDP report documents widespread use of insecure, public‑cloud‑based AI tools by lawyers and court staff—a practice that risks massive data breaches and violates the Act. Courts’ understandable reluctance to share data with external researchers has also created a barrier to independent, peer‑reviewed evaluation.

Opacity and Unauditability

Details concerning the training data, model architecture, hyperparameters, performance benchmarks, and error logs of judicial AI tools (SUPACE, SUVAS, LegRAA) are not publicly available. Hallucination rates, accuracy on regional‑language inputs, and decision‑making logic remain opaque. Without audit‑grade documentation, independent verification is impossible, and courts themselves lack the technical capacity to assess whether an AI tool is fit for purpose.

Algorithmic Bias and Representation Gaps

Training datasets are, by construction, drawn from a historical record that reflects structural inequalities of caste, class, and gender. Under‑represented dialects—such as those spoken by Adivasi communities—may be entirely absent from speech‑recognition training corpora. The Supreme Court White Paper explicitly warns that without affirmative corrective measures, AI systems risk digitally cementing caste‑based discrimination and producing outputs that systematically disadvantage already‑marginalised litigants.

Infrastructure and Resource Constraints

High‑performance AI models require specialised hardware (GPUs/TPUs), robust internet connectivity, and skilled technical personnel—resources that are scarce in district and rural courts. Evaluations to date rely predominantly on anecdotal reports of reduced research time, with no hard data on case‑disposal rates, error frequencies, or downstream impacts on fundamental rights. No standardised methodology currently exists to measure the systemic effects of AI deployment across courts.

Way Ahead

To address the data challenges and institutional gaps identified above, senior judges, the Supreme Court e‑Committee, the IndiaAI Mission, and a growing body of academic and civil‑society research have converged around a three‑pronged strategy, aligned with e‑Courts Phase III funding and the IndiaAI Mission’s governance architecture, aiming for responsible AI scaling by 2027–2030.

Standardising and Harmonising Available Data

• Uniform Metadata Standards: The National Informatics Centre, in collaboration with the Supreme Court e‑Committee, should mandate a common data schema across all e‑Courts Phase III platforms, including standardised, anonymised fields for litigant characteristics (caste‑category, gender, district) to enable disaggregated bias testing.
• AIKosh‑Based Centralisation: All anonymised, non‑personal court data suitable for AI training should be deposited in the IndiaAI Dataset Platform (AIKosh), with tiered access protocols (open, restricted, classified) and a formal memorandum of understanding between the e‑Committee and MeitY to govern data flow.
• Legacy Data Audits: Before being ingested into any AI training pipeline, legacy datasets must undergo bias and fairness audits by an independent, inter‑disciplinary panel, employing techniques such as differential privacy.
• Court‑Owned, DPDP‑Compliant Repositories: Consistent with the Supreme Court CRP White Paper’s recommendation, training data for judicial AI must be owned and controlled by the courts, stored on secure, in‑house servers, and never shared with commercial vendors without binding data‑processing agreements that comply fully with the DPDP Act, 2023.

Improving Future Data Collection

• Privacy‑by‑Design: Build automatic redaction of personally identifiable information and consent frameworks for non‑sensitive data directly into the e‑filing and case‑management workflow. IIT Madras’s pilot metadata‑extraction tool demonstrates technical feasibility.
• AI‑Assisted Quality Control: Deploy lightweight AI tools to detect errors, enrich metadata, and flag missing fields at the point of case filing, reducing downstream data‑cleaning costs.
• Inclusive and Multilingual Data Gathering: Launch a targeted campaign—funded through the Bhashini Mission and e‑Courts Phase III—to build dialect‑aware speech‑recognition corpora and standardised legal‑terminology glossaries for all 22 languages and major vernacular dialects, with systematic over‑sampling of marginalised communities.
• Formal Data Governance Policies: Every High Court should adopt a published Data Governance Policy specifying acquisition procedures, quality‑control checkpoints, version‑control protocols, and retention schedules.
• Judicial Data Labs: In coordination with IndiaAI, establish secure, sandboxed research environments—Judicial Data Labs—where accredited academic and civil‑society researchers can access curated, anonymised datasets under strict access agreements, bridging the research‑practice gap identified in Section 5.2.

Enabling Systemic Analysis

• Adopt a Multi‑Dimensional Evaluation Framework: The DAKSH–UNDP framework—covering institutional readiness, rights‑impact by use case, technical benchmarking, operational integration, and ongoing monitoring with bias‑tracking—should be formally adopted by the e‑Committee as the standard pre‑deployment and post‑deployment assessment methodology for all judicial AI tools.
• National AI Incidents Database: Prioritise the establishment of the National AI Incidents Database, proposed by both the MeitY Guidelines and the PSA White Paper, with a mandate to collect, classify, and publish annual reports on AI failures, biased outcomes, and security breaches in the justice sector, following OECD‑compatible incident definitions to enable cross‑country comparison.
• Mandatory Explainability and Human Oversight: Enact binding rules—via delegated legislation under the DPDP Act or through a judicial‑conduct code—requiring that (a) every judicial AI tool produce human‑interpretable explanations for its outputs, and (b) a human judicial officer remains the final decision‑maker in every case, with no fully autonomous AI determination permitted.
• Independent, Public Audits: All judicial AI tools should undergo annual independent algorithmic audits, conducted by empanelled firms and technical institutions, with audit reports made public in anonymised form and vendors held contractually accountable for remedial action.
• Pilots and Longitudinal Studies: Fund and conduct multi‑year longitudinal studies, in academic collaboration, that track not only efficiency metrics but also equity outcomes—including differential impacts on Scheduled Caste, Scheduled Tribe, and women litigants—before any tool progresses beyond pilot phase.
• Institutional Capacity Building: Establish AI Ethics Committees in every High Court; provide mandatory, continuing judicial education on AI capabilities, risks, and rights‑compatible deployment; and allocate dedicated funds for digital infrastructure in rural and district courts, without which the promise of AI‑enabled access‑to‑justice will remain a metropolitan conceit.

The consensus among all stakeholders—from the Chief Justice of India’s public statements to the DAKSH–UNDP report—is unequivocal: AI must remain a support tool, never a substitute for judicial reasoning. Data sovereignty, algorithmic transparency, and the protection of fundamental rights under Articles 14 and 21 are the non‑negotiable pillars of any future pathway. This roadmap, embedded in the e‑Courts Phase III vision and the IndiaAI governance architecture, seeks to harvest the efficiency gains of AI while resolutely safeguarding the constitutional promise of equal justice for all.