The Supreme Court’s AI Draft Needs A Tech Reform

But for courts, the central question cannot stop at whether AI is assistive or decisional. As a technology lawyer, the harder question is whether the AI system can be explained, tested, logged, audited, challenged, rolled back and remedied when something goes wrong. Human oversight is necessary, but it is not a magic phrase. If the human reviewer has no record of the model version, no evaluation history, no prompt trail, no retrieval source, no confidence threshold and no incident log, oversight becomes theatre with a human face.

This distinction matters because AI failure rarely announces itself as a constitutional problem. It may appear first as a bad translation, a missing paragraph in a summary, a fabricated citation, a retrieval system surfacing the wrong precedent, a speech-to-text error in a witness deposition, a model update that changes output behaviour, or an access-control lapse that exposes sensitive judicial data. By the time the legal system calls it prejudice, the technical trail may already be gone.

The draft gets several instincts right. It speaks of human primacy, judicial independence, fairness, transparency, explainability, accountability, auditability, data protection, purpose limitation, proportionality, accessibility, data integrity and cybersecurity. It also creates institutional mechanisms around AI registers, controlled environment testing, audits, incident databases, fallback protocols, disclosure, content verification, procurement, sensitive judicial data, training, grievance redressal and remedies. These are not minor drafting choices. They show that the Court is trying to govern AI as infrastructure, not as a fashionable productivity tool.^[3][4]

The next step, however, is to make auditability operational. In engineering terms, an audit is not a general assurance that a system was reviewed. It is a record of what was deployed, what data or retrieval layer it used, which version generated the output, who accessed it, what controls were applied, how it was tested, what exceptions occurred, and what decision was taken after the exception was noticed. In legal terms, that same record becomes evidence, accountability and remedy.

This is where the current draft needs sharpening. It uses the right vocabulary, but the final regulations should specify the minimum artefacts that every high-impact judicial AI system must maintain: model or system cards, version histories, evaluation reports, audit logs, access logs, data provenance records, prompt and output retention policies, incident reports, red-team findings, human override records, rollback procedures and vendor change notices. Without these artefacts, a court may know that an AI tool was used, but not whether it was used safely.

The point is not to drown court administration in paperwork. The point is to create a defensible decision trail. A court that uses AI for transcription should know the error rates across languages, dialects and audio quality. A court that uses AI for translation should know whether legal terms are being preserved across Indian languages. A court that uses AI for legal research should know whether the tool is generating text from a closed, verified corpus or guessing from a general model. These are legal questions because they decide who bears the cost of technical uncertainty.

Comparative frameworks point in the same direction. NIST treats AI risk management as a lifecycle exercise of governance, mapping, measurement and management, not as a one-time declaration of safety. CEPEJ's judicial AI charter emphasises fundamental rights, non-discrimination, quality, security, transparency, fairness and user control, while UNESCO has repeatedly framed courts as both users of AI and guardians against its misuse. The EU AI Act's risk-based architecture and judicial guidance from the UK and Canada also reflect the same basic lesson: high-impact AI requires documentation, verification, confidentiality discipline and non-delegation of judicial responsibility.^[5][6][7]

India should not copy these models mechanically. Indian courts operate at a scale, linguistic diversity and backlog reality that most jurisdictions do not. The better approach is to adapt the design lesson: low-risk tools can move quickly, but rights-affecting systems must be auditable before they become ordinary.

The first reform should be a judicial AI assurance layer. Every court-approved AI system should sit within a documented lifecycle: pre-deployment testing, approval, controlled release, monitoring, incident escalation, periodic review, version change approval and retirement. This is basic systems governance. A model that changes every few weeks cannot be governed like a static rulebook; its behaviour must be tracked over time.

The second reform should be independent technical audit for high-impact systems. Internal review is necessary, but it cannot be the entire accountability model where AI affects liberty, evidence, listing, translation, accessibility, judicial records or sensitive data. Independent audit need not mean public disclosure of source code or security-sensitive material. It can be structured through confidentiality rings, expert panels and controlled access. But a closed loop of approval, deployment and audit will not create public confidence.

The third reform should be a litigant-facing rights layer. The draft provides grievance mechanisms, but an affected person needs more than a complaint window after harm occurs. Where AI materially affects a court process, the litigant should have notice, a meaningful description of the AI system's role, access to the relevant non-confidential record, an opportunity to contest or correct AI-assisted output, human review, and a remedy where prejudice is shown. This is not a demand that every litigant inspect every model. It is a demand that no one should be asked to challenge a shadow.^[8]

The fourth reform should be procurement discipline. AI vendors should not be treated like ordinary software suppliers. The draft already recognises obligations around private entities and sensitive judicial data. The final regulations should go further by requiring model update controls, audit cooperation, breach reporting, no training on court data without express approval, data localisation or transfer safeguards where appropriate, deletion commitments, portability, subcontractor visibility, security testing and clear liability allocation. Judicial data is not training fodder.^[9]

The fifth reform should be public transparency that is meaningful but not reckless. A public AI register should not expose vulnerabilities, confidential datasets or security architecture. But it should disclose enough to allow institutional scrutiny: the tool name, purpose, deployment court, vendor, category of use, approval date, risk tier, audit status, incident count, data categories processed and whether an impact assessment summary is available. The current draft's register, audit and transparency-report provisions provide the base; the final version should make the public layer harder to avoid.^[10]

The strongest counterargument is practical. Courts are overburdened, and AI can reduce delay. If every tool is burdened with excessive process, adoption may become slower than the problem it is meant to solve. That concern is fair, but it supports risk-tiered governance, not weak governance. Grammar tools, scheduling aids and accessibility support should not be treated like AI that affects bail, evidence, translation, listing or judicial reasoning.

This is also why disclosure must be calibrated. Lawyers, judges and court staff should not be punished merely for using AI responsibly. A lawyer using AI to format a chronology or test a research query is not the problem. The problem is unverified AI output presented as law, confidential data entered into unsafe tools, undisclosed material AI assistance, and systems whose influence cannot be reconstructed later. The regulatory question is narrower and more important: when AI materially affects a legal submission or court process, can the system produce a reliable trail? The final regulations should therefore create a judicial AI assurance stack: risk classification, approved use cases, system documentation, data provenance, benchmark testing, red-teaming, audit logs, access controls, version control, incident reporting, independent audit, public transparency, human override and litigant remedy. These are not engineering luxuries. In a court system, they are due process infrastructure.

The Supreme Court deserves credit for opening this conversation early and seriously. The next version can make India one of the first jurisdictions to govern judicial AI with both constitutional imagination and technical discipline. That would be the real contribution: not saying that AI is forbidden, and not saying that AI is inevitable, but saying that AI may assist justice only when it can be accounted for. AI in courts must be auditable because justice itself must be auditable. If a system cannot be tested, logged, explained, challenged or corrected, it should not quietly become part of the machinery through which rights are processed. The court may use technology, but technology must remain answerable to the court, the Constitution and the people whose disputes pass through it.