Author: Sakshi Tripathi, United University, Prayagraj
To the Point
Algorithmic sentencing, the use of artificial intelligence (AI) and predictive analytics to aid or determine judicial sentencing decisions, is gaining momentum globally. As India considers integrating algorithmic tools in its criminal justice system, the legal fraternity must grapple with a central question: can these technologies uphold constitutional values, or do they present a danger to due process, equality, and judicial judgment?
Abstract
The deployment of algorithmic sentencing—where artificial intelligence (AI) aids or determines judicial sentencing—signals a significant shift in the Indian criminal justice landscape. While global examples like the U.S. COMPAS system show both the benefits and perils of such technologies, India must consider its unique constitutional and socio-legal context. This article examines the feasibility of implementing algorithmic tools in Indian sentencing by assessing their alignment with the Constitution of India, especially regarding due process and equality under the law, and judicial discretion.
The abstract evaluates both the potential efficiencies AI offers—such as consistency, speed, and reduced judicial backlog—and the significant risks it entails, such as bias amplification, data opacity, and undermining of individualized justice. Key constitutional doctrines, including the principles of natural justice, proportionality, and non-arbitrariness, serve as critical benchmarks in this analysis. Drawing on comparative case studies and landmark Indian judgments, the article argues that while algorithmic sentencing might represent a technological frontier, its unregulated use may infringe upon the foundational values of the Indian legal system. It concludes by emphasizing the need for legal safeguards, transparency mandates, and human oversight as prerequisites for any future adoption.
Use of Legal Jargon
Due Process: The constitutional assurance that legal processes will be just and that people will receive notice and a chance to present their case.
Natural Justice: A legal philosophy used in some jurisdictions to ensure fair decision-making.
Wednesbury Unreasonableness: A principle used to review administrative decisions for irrationality.
Discretionary Jurisprudence: The framework within which judicial officers exercise discretion in sentencing.
Presumption of Innocence: A core principle of criminal law that assumes an accused is innocent until proven guilty.
Proportionality Doctrine: The idea that the punishment must fit the crime.
Non-Arbitrariness: A cornerstone of Article 14, which prohibits arbitrary state action.
The Proof
The potential for algorithmic sentencing to be integrated into India’s criminal justice system is supported by a confluence of legal, technological, and policy developments. Globally, tools like COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) in the United States and the Harm Assessment Risk Tool (HART) in the United Kingdom have demonstrated how predictive analytics can influence judicial decision-making.
In India, NITI Aayog’s 2018 discussion paper on the National Strategy for Artificial Intelligence signals a keen governmental interest in leveraging AI across sectors, including law enforcement and the judiciary. Additionally, the draft Digital Personal Data Protection Bill, 2022, while primarily aimed at data governance, reflects an increasing readiness to regulate digital and algorithmic tools in governance.
Empirical studies from abroad provide mixed results. For instance, a ProPublica investigation into COMPAS revealed racial bias against African American defendants, casting doubt on the neutrality of algorithmic tools. On the other hand, advocates argue that algorithmic models can increase consistency in sentencing by minimizing human error and subjective bias.
Legal scholars and technologists warn, however, that algorithmic decision-making, when applied to sentencing, risks violating fundamental rights unless subjected to strict oversight. Algorithms can suffer from “black box” opacity—meaning their reasoning processes are often inscrutable to both judges and defendants. This raises significant concerns about transparency and the ability to challenge adverse outcomes, a key component of due process under Article 21 of the Constitution.
Furthermore, sentencing is not merely a mechanistic exercise; it involves evaluating mitigating and aggravating circumstances, offender background, remorse, and potential for rehabilitation. These qualitative dimensions are difficult to quantify and may be poorly captured by algorithms trained on historical data that reflect existing social prejudices.
The Indian judiciary has also signaled a measured openness to tech-based reforms, as evidenced by the introduction of tools like SUVAAS (Supreme Court Vidhik Anuvaad Software) for translation and the digitization of case management systems. However, no formal algorithmic sentencing tools have yet been approved or used.
Therefore, the proof suggests that while algorithmic sentencing is technologically plausible and even legally inevitable, its current state—marked by bias, opacity, and lack of accountability—makes it incompatible with India’s constitutional principles unless comprehensive safeguards are instituted.
Case Laws
Maneka Gandhi v. Union of India (1978 AIR 597): Established that the ‘procedure established by law’ must be just, fair, and reasonable, forming the bedrock for any algorithmic tool to conform to due process.
Justice K.S. Puttaswamy (Retd.) v. Union of India (2017 10 SCC 1): Affirmed the right to privacy as a fundamental right, which raises concerns about the data collection and predictive analytics employed by algorithmic sentencing tools.
State of Punjab v. Jagir Singh (1974 AIR 370): Highlighted the importance of human discretion and judicial reasoning, which may be undermined by algorithmic tools.
Bachan Singh v. State of Punjab (1980 2 SCC 684): Introduced the ‘rarest of rare’ doctrine for the death penalty. The judgment emphasized individualized sentencing, which could be jeopardized by generic algorithmic outputs.
Selvi v. State of Karnataka (2010 7 SCC 263): The court ruled against the involuntary administration of scientific techniques in criminal investigations, cautioning against over-reliance on technology.
Anuj Garg v. Hotel Association of India (2008 3 SCC 1): This case warned against the use of stereotypical and regressive assumptions in lawmaking—a caution relevant to AI trained on biased historical data.
State of Rajasthan v. Union of India (1977 3 SCC 592): Reaffirmed the basic structure doctrine, underscoring that any technological intrusion into the legal system must respect the constitutional framework.
Shreya Singhal v. Union of India (2015 5 SCC 1): Emphasized clarity in legal standards and condemned vague laws, a principle applicable to algorithmic opacity.
PUCL v. Union of India (1997 1 SCC 301): Reinforced the right to privacy and the safeguards required before conducting surveillance, relevant for algorithmic tools collecting and analyzing sensitive personal data.
Mohd. Arif v. Supreme Court of India (2014 9 SCC 737): Asserted that even in death penalty cases, due process and review are crucial—reiterating the role of human oversight in severe sentencing decisions.
Conclusion
Algorithmic sentencing in India represents a critical inflection point where innovation intersects with the Constitution. Though the use of AI in judicial functions promises efficiencies—such as minimizing arbitrary sentencing and reducing court backlogs—it simultaneously raises profound constitutional and ethical concerns.
The most significant risks include opaque decision-making, the perpetuation of social and economic biases embedded in training data, and the dilution of human discretion which is central to the philosophy of justice. The Indian legal system, founded on principles like fairness, proportionality, and individualized justice, must resist any technology that undermines these ideals.
Judicial precedents have consistently emphasized the inviolability of fundamental rights, including due process (Article 21), equality (Article 14), and privacy. Any encroachment by AI tools into sentencing must be subjected to rigorous scrutiny under these constitutional lenses. Without transparency, explainability, and human oversight, algorithmic tools risk becoming instruments of injustice rather than reform.
Hence, the way forward is a cautious, phased integration of algorithmic tools—strictly as advisory mechanisms and not determinative systems. This approach must be supported by clear legislative mandates, strong data protection laws, pilot-based assessments, and continuous judicial training. In sum, algorithmic sentencing can be India’s next legal frontier, but only if approached with constitutional fidelity and an unwavering commitment to human rights.
FAQS
Q1: What is algorithmic sentencing?
A: Algorithmic sentencing involves using artificial intelligence and data-driven tools to assist judges in determining appropriate criminal sentences based on past data, risk assessment, and predictive analytics.
Q2: Has India implemented algorithmic sentencing yet?
A: No, but there is growing interest from policy bodies such as NITI Aayog. Pilot programs and discussions are underway, but a formal implementation framework is yet to be developed.
Q3: What are the constitutional concerns?
A: Key concerns include potential violations of the right to equality (Article 14), right to life and personal liberty (Article 21), and the right to privacy. Algorithmic tools could also impair judicial discretion and individualized justice.
Q4: Can algorithmic sentencing improve the criminal justice system?
A: If implemented cautiously, it can aid in reducing inconsistencies, speeding up the process, and minimizing human error. However, it must be transparent, auditable, and aligned with constitutional values.
Q5: How have other countries dealt with algorithmic sentencing?
A: In the U.S., tools like COMPAS have been controversial due to racial bias. In the UK, pilot tools have been more regulated. Lessons from these jurisdictions underscore the need for strong legal safeguards.
Q6: What are the alternatives?
A: Enhancing judicial training, improving sentencing guidelines, and using AI strictly for research and administrative assistance—not decision-making—are safer interim measures.
Q7: What should be the way forward for India?
A: India should adopt a phased approach with regulatory oversight, data protection laws, judicial training, and clear opt-out provisions for defendants. Algorithmic tools should remain advisory, not determinative.
Q8: How can algorithmic bias affect sentencing?
A: Algorithms trained on historical data may replicate existing societal and judicial biases, leading to disproportionate sentencing for marginalized communities.
Q9: Is there a mechanism to audit or review algorithmic decisions?
A: Currently, India lacks a formal framework for algorithmic audits in the judicial context. Developing explainable AI systems and independent oversight mechanisms is essential.
Q10: Could algorithmic sentencing reduce judicial workload?
A: Potentially yes, by assisting in standardizing routine decisions. However, without proper human oversight, it risks substituting efficiency for justice.
Q11: What role does the judiciary play in regulating algorithmic tools?
A: The judiciary must ensure any technological intervention aligns with constitutional values and maintain ultimate control over sentencing to preserve the spirit of justice.
Q12: Are there any existing Indian initiatives for AI in law?
A: Yes, initiatives like SUVAAS (Supreme Court Vidhik Anuvaad Software) and AI-powered transcription tools are in place. However, they are focused on administrative efficiency rather than judicial decision-making.
