AI for Justice: Ethical, Fair and Robust Adoption in India’s Courts

AI for Justice: Ethical, Fair and Robust Adoption in India’s Courts

In recent years, artificial intelligence (AI) has increasingly been used in India’s courts for tasks such as transcription, translation, and legal research. Adoption has been gradual, and judges have approached these technologies with caution, consistently emphasising that AI cannot and should not replace human decision-making.

AI systems function through data inputs, computational models, and generated outputs. Each of these components raises concerns about accuracy, fairness, and privacy. As custodians of highly sensitive information, courts are right to proceed carefully. Risks such as bias, hallucinations, limited transparency, and gaps in underlying datasets can undermine judicial legitimacy and public confidence.

At the same time, the absence of clear guidelines for evaluating both the effectiveness and risks of AI tools can lead to decision paralysis or poorly informed adoption. Our research finds that many courts still lack the technical expertise and structured impact-assessment processes needed to determine whether AI integration is genuinely improving efficiency or access to justice.

Significant structural challenges also remain. The lack of digitised legacy records and reliable metadata creates data gaps, increasing the likelihood that AI systems trained on available datasets will replicate or amplify existing inequalities. In many instances, AI adoption is driven by individual champions within the judiciary, making initiatives vulnerable to disruption due to transfers or retirements. These efforts also unfold in a context of already stretched administrative capacity. Taken together, these factors risk weakening public trust and raising concerns about the judiciary’s legitimacy as AI use expands.

The use of AI in courts also carries the potential for rights violations. Such harms may arise not only from training data and model design, but also from the institutional and procedural realities of the legal system, including:

• The nature of the judicial function (for example, translation versus judgment drafting)
• The type of case or proceeding (such as privacy concerns in sensitive matters)
• The characteristics of litigants (including vulnerable witnesses or marginalised communities)

Courts therefore need structured frameworks to guide AI adoption. These should include tools to assess institutional readiness, strategies to mitigate risk, guidance on key questions for AI vendors, and checklists for post-adoption monitoring. This report also proposes indicative contractual safeguards to protect data and clarify accountability, along with recommendations to strengthen courts’ technological capacity through the creation of dedicated expert cadres.

While formal processes are essential for institutional adoption, standardised procedures may not always be practical for individual uses of AI in day-to-day judicial work, especially given the rapid proliferation of generative AI tools used for research and drafting. For this reason, the report also sets out clear dos and don’ts for users of generative AI within the judicial system.