Author: Gargi Koreti
To the Point
The increasing administrative load, case backlogs and persistent delays within the judicial system are the primary drivers for adopting Artificial Intelligence (AI). AI offers a promise of efficiency by automating routine judicial tasks, such as document sorting, locating precedents, translation and scheduling. It also enables judges to access information more rapidly.The Core Conflict: AI vs. Judicial Independence
The use of AI becomes contentious when its function moves from administrative support to influencing or shaping the actual process of judicial decision-making. The Constitution mandates that the judiciary must exercise independent legal reasoning. Any AI tool, particularly those involved in prediction or scoring, that affects final outcomes raises concerns about potential bias and the distortion of fairness.Constitutional Concerns for AI in Indian Judiciary
The application of AI in the Indian judiciary must remain compliant with key constitutional provisions:
Article 14 (Equality): AI use must not lead to arbitrariness or discriminatory practices.
Article 21 (Right to Life and Liberty): The integration of AI must uphold due process and the fundamental right to be heard.
Article 50 (Separation of Powers): The independence of the judiciary from the executive must be preserved, and AI’s function must not threaten this separation.
Any deployment of AI that jeopardizes these constitutional safeguards is inherently suspect.
Use of Legal Jargon
For legal clarity and to analyze AI-enhanced decision-making against constitutional standards, this paper employs the following key doctrinal terms:
Core Principles of Fairness and Justice:
Natural Justice: Encompasses fairness, the right to be heard (Audi Alteram Partem), and the requirement for reasoned orders.
Due Process: Refers to procedural fairness, as guaranteed under Article 21 of the constitution.
Presumption of Innocence: A fundamental principle of criminal law.
Constitutional and Judicial Review Concepts:
Judicial Review: The power of courts to assess the legality and fairness of decisions.
Proportionality Test: A constitutional method for balancing individual rights against state interests.
Manifest Arbitrariness: A constitutional ground, under Article 14, for invalidating state actions.
Non-Delegation Doctrine: The principle that adjudicatory authority cannot be transferred to non-judicial bodies.
AI and Algorithmic Specific Terms:
Opacity / Black-Box Algorithms: Describes systems where the internal logic for decision-making is inexplicable.
Algorithmic Bias: Refers to discriminatory outcomes produced by artificial intelligence models.
The Proof
I. Benefits of AI in Judicial Decision-Making
1. Efficiency and Speed
AI tools reduce the time spent on repetitive tasks. For example, automated transcription, research suggestions, and case tagging free judges to focus on core judicial functions. Courts with heavy pendency, such as India, benefit significantly from such support. When used properly, AI shortens timelines without compromising fairness.
2. Enhanced Consistency
One of the criticisms of judicial systems is the inconsistency of outcomes across courts or judges. AI trained on large datasets can flag deviations or suggest standardised formats. This does not mean AI should dictate the outcome, but it can act as a reference point to reduce excessive disparity.
3. Strengthening Access to Justice
For litigants without legal representation, AI-based helpdesks or chatbots offer basic guidance on filing procedures, required documents, and deadlines. Such tools democratise access to information and reduce procedural exclusions. They also allow courts to handle more matters without increasing staff.
4. Supporting Data-Driven Judicial Reforms
AI analytics reveal patterns in delays, sentencing disparities, case backlogs, or overburdened courts. These insights support policy decisions—such as creating fast-track courts, improving staffing, or modifying procedures. AI datasets can also highlight systemic bias that may not be visible in individual cases.
II. Risks Associated with AI in Judicial Decision-Making
1. Algorithmic Bias and Article 14 Violations
AI systems learn from historical data. If historical decisions reflect bias against marginalised communities, the algorithm will replicate and even amplify that bias. The U.S. experience with the COMPAS tool showed that Black defendants were rated “high-risk” more often than white defendants despite similar criminal histories. Such discrimination would violate Article 14 in India as manifest arbitrariness and hostile discrimination.
2. Non-Explainability (Black-Box Problem)
Many AI models cannot explain how they reached a conclusion. Judicial decisions, however, require reasoned orders that can be reviewed on appeal. If AI influences a decision without providing an explanation, it violates:
Audi Alteram Partem,
the right to a reasoned order, and
transparency under Anuradha Bhasin.
Opaque algorithms undermine public trust and the principle that justice must be both done and seen to be done.
3. Delegation of Judicial Power (Article 50)
Judicial functions cannot be outsourced. If a judge relies heavily on an AI-generated score for bail, sentencing, or conviction patterns, it amounts to indirect delegation. This violates the principle of judicial independence, as the decision-maker becomes the algorithm rather than the judge’s trained reasoning.
4. Violation of Informational Privacy (Puttaswamy)
AI tools require vast datasets, including criminal records, socio-economic details, behavioural data, and personal identifiers. Without strong safeguards, this information risks:
misuse,unauthorised access,data breaches, or
function creep.
The Supreme Court in Puttaswamy held that any invasion of privacy must satisfy legality, necessity, and proportionality. Judicial AI tools must meet this test.
5. Lack of Accountability
If an AI tool leads to a wrong or unjust result, it becomes difficult to identify responsibility. Possibilities include:
the judge for relying on the tool,
the programmer who created the model,
the institution that purchased it.
This diffuse accountability contradicts the constitutional requirement of judicial responsibility. Courts cannot hide behind algorithms when rights are affected.
III. Constitutional Concerns
1. Article 14 – Equality and Non-Arbitrariness
Judicial decisions must be free from arbitrariness. AI systems trained on skewed data inherently produce unequal outcomes. Even seemingly neutral variables—such as location, income, or past arrest records—act as proxies for caste, religion or socio-economic status. This violates both equality before law and equal protection.
2. Article 21 – Due Process and Fair Procedure
AI in courts must respect procedural fairness:
litigants must understand the basis of decisions,
they must be able to challenge adverse findings,
decisions must provide reasons.
Any AI system that undermines these principles violates Article 21.
3. Article 50 – Independence of Judiciary
AI tools built by private corporations or government agencies may subtly influence judicial reasoning. Judges cannot become passive adopters of algorithmic guidance. Judicial independence requires human application of mind, contextual understanding, and empathy.
4. Open Justice and Transparency
Courts operate publicly to ensure accountability. If AI tools are:
proprietary,
confidential,
protected by trade secrets,
then their logic becomes shielded from scrutiny. This contradicts the fundamental principles of open courts and transparency.
5. Non-Delegation Doctrine
Core judicial functions—interpretation, application of law, and evaluation of evidence—cannot be mechanised. Even if AI suggests an outcome, the final reasoning must come from a human judge. Delegating these powers to an algorithm violates separation of powers.
IV. Global Approaches and Indian Context
1. The United States
Some states experimented with risk assessment tools like COMPAS in sentencing and bail. After public reports revealed racial bias, judicial reliance on such tools reduced. Courts now demand transparency and accuracy.
2. European Union
The EU’s AI Act (2024) classifies judicial AI systems as “high-risk.” These systems must meet strict obligations concerning:
transparency,
explainability,
human oversight,
data quality.
3. India’s Approach
India has taken a cautious route:
The Supreme Court uses neutral AI tools like transcription (e.g., SUVAAS).
Kerala High Court and other states have issued guidelines prohibiting AI from influencing outcomes.
AI is limited to administrative or assistive functions.
This approach aligns with constitutional safeguards and global good practices.
Abstract
Artificial Intelligence (AI) is transforming judicial systems globally, with courts utilizing AI tools for functions like research, case management, translation, transcription and even risk assessment in bail and sentencing in certain jurisdictions. These advancements promise benefits such as faster case resolution, greater consistency and improved support for unrepresented litigants.
However, the integration of AI into core judicial decision-making introduces significant constitutional challenges. These concerns revolve around potential algorithmic bias, lack of transparency in decision-making, infringement of due process and privacy rights, issues of accountability and the risk of compromising judicial independence.
This article offers an analysis of the advantages and perils of AI in judicial processes, employing semi-formal language, precise legal terminology and constitutional principles. It examines global and Indian developments, referencing landmark case laws including Puttaswamy, Anuradha Bhasin and Rajesh Gautam.
The central conclusion is that AI should serve as an aid to, rather than a replacement for, human judicial reasoning. To uphold the mandates of Articles 14, 21, and 50 of the Indian Constitution, the deployment of AI must be subject to stringent safeguards: mandatory transparency, explainability, robust data protection, continuous human review and an absolute prohibition on fully automated judicial outcomes. A balanced, rights-respecting and human centric strategy is essential for the future of AI in the judiciary.
Case Laws (related judgments and precedents)
1. State of Uttar Pradesh v. Rajesh Gautam (2020)
The Supreme Court emphasised the need for individualised judicial reasoning in bail decisions, which cannot be replaced by standardised algorithmic scores.
2. Justice K.S. Puttaswamy v. Union of India (2017)
Recognised informational privacy as part of Article 21, when courts adopt data-hungry AI systems.
3. Suresh Kumar Koushal v. Naz Foundation (2013) (Context of majoritarian data)
Held that judicial review cannot be driven solely by statistical representations; similarly, AI models relying on historical bias cannot dictate outcomes.
4. Anuradha Bhasin v. Union of India (2020)
Reinforced the need for transparency and reasoned orders, incompatible with black-box algorithmic decisions.
5. K.M. Nanavati v. State of Maharashtra (1961)
Highlights the importance of judicial discretion, human evaluation and contextual reasoning that principles in conflict with automated decision-making.
Conclusion
AI in the Indian Judiciary: Supporting Justice with ‘Human-in-Command’ Oversight
Artificial Intelligence holds significant potential to enhance the Indian judicial system by improving access, boosting efficiency, and strengthening legal research. Crucially, the deployment of AI must be strictly assistive, adopting a “human-in-command” philosophy where technology supports, but never substitutes, the essential role of judicial discretion.
To ensure responsible and ethical integration, the following clear steps are necessary:
Core Principles for AI Governance:
Strict Prohibition on Outcome Determination: AI must not be used to determine core adjudicatory questions, such as guilt, innocence, bail or sentencing. Judges must remain the final decision-makers.
Robust Data Protection: Strong safeguards, aligned with principles like those articulated in Puttaswamy, must protect sensitive judicial data.
Mandatory Transparency and Auditability: All AI usage in courts must be public, reviewable, open to challenge, and accountable.
Pre-Deployment and Operational Requirements:
Algorithmic Impact Assessments (AIA): A mandatory evaluation of bias, accuracy and data quality must be conducted before any AI tool is deployed.
Explainability and Reasoning: AI tools utilized in judicial settings must be capable of generating clear, understandable and articulable reasons for their output.
FAQs
1. Can AI legally decide a case?
No. Delegating judicial power to AI violates Articles 14, 21, and 50 and the doctrine of judicial independence.
2. Can AI be used for bail or sentencing?
Only as a non-binding reference. Courts must record independent, reasoned orders.
3. Is using AI for legal research permissible?
Yes. Research assistance, drafting help, translation and summarisation are allowed as long as judges apply their mind.
4. What is the biggest constitutional issue?
Lack of transparency and explainability, which violates due process under Article 21.
5. Are Indian courts using AI today?
Yes, but only for administrative functions like transcription, translation, and e-filing support ,not for deciding cases.
