INTRODUCTION
Who should hold the power to decide an individual’s personal liberty? As modern criminal trials are becoming more complicated, bail functions as far more than a standard procedural formality. It fundamentally dictates whether someone legally presumed innocent walks free pre-trial or remains locked up. Granting judges this discretionary power ensures vital flexibility and necessary adaptability, yet it frequently draws sharp criticism for producing arbitrary outcomes, systemic unfairness, and deep personal bias.
To enhance consistency, several jurisdictions have been using algorithmic Risk Assessment Instruments (RAIs)[1] to assist in making decisions. These tools analyze the historical data and help in predicting instances where an accused may fail to appear in court or commit another offence while on release. But another question arises: can a person meaningfully challenge a recommendation produced by an algorithm, whose reasoning is not fully transparent? As technology is deeply connected with criminal justice, the real challenge is not choosing between innovation and due process, but ensuring that technological efficiency never comes at the cost of fairness, transparency and individual liberty.
ALGORITHMIC DECISION MAKING AND DUE PROCESS
Modern risk assessment tools, like the US Public Safety Assessment (PSA), aim to standardize pre-trial decisions by categorizing accused people into statistical risk tiers to guide decisions regarding bail. This assists judges in introducing better consistency into pre-trial decision-making, particularly in jurisdictions with heavy caseloads that require speedy resolution of bail applications within limited time frames.
However, introducing these automated systems in India directly clashes with the rulings of the Constitution. Under Article 21 and the Bharatiya Nagarik Suraksha Sanhita (BNSS), judges must assess each case on its own facts by evaluating actual flight risk, offense severity, and witness safety rather than relying on automated scores. Since these tools learn from historical records, they may easily repeat old police biases against poor and minority groups. This weakens the basic principle that bail is the rule and prison is the exception.[2]
Justice cannot be attained on the basis of efficiency. As defined in Article 21[3] of the Constitution of India and Articles 5 of the European Convention on Human Rights, every deprivation of liberty must result from a procedure that is fair, transparent and reasonable. This principle was recognized in Maneka Gandhi v. Union of India,[4] where the Supreme Court held that any procedure must meet standards of fairness rather than mere statutory compliance.
From this perspective, the algorithmic tools should be intended to be used as an aid to judicial reasoning rather than serving as substitutes. The constitutional legitimacy lies in the fair opportunity, not in sophisticated technology.
ALGORITHMIC TRANSPARENCY AND THE CHALLENGE OF COMMERCIAL SECRECY
Using algorithmic software to decide bail raises serious concerns about procedural transparency that extend beyond accuracy. Many private software developers treat source code and training datasets as trade secrets. However, an accused person can be classified as high risk without ever understanding the underlying rationale, directly threatening their personal liberty.
A key example is the US decision in State v. Loomis,[5] where the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) risk assessment tool was challenged on due process grounds. Although not directly applicable in India, it offers crucial persuasive guidance on how automated risk assessments affect an accused person’s constitutional right to challenge adverse evidence. In that case, although the court upheld the software’s use, it made it clear that algorithmic assessments should only assist judicial decision-making and not be treated as conclusive proof. The court further advised the judges to remain aware of the system’s limitations when relying upon its recommendations.
Yet, while the judgment preserved the practical utility of the technology, it highlighted an unsolved constitutional dilemma of how an individual can effectively challenge evidence generated by a system whose internal reasoning remains a black box.
Under the Indian Constitution, detention similarly requires meaningful disclosure. In Pankaj Bansal v. Union of India,[6] the Supreme Court held that Article 22(1) mandates providing clear, written grounds for the deprivation of liberty. Without understanding how the software calculated their risk score, an accused person cannot meaningfully defend themselves, rendering such detention unfair under Article 14 and unlawful under Article 21.
Natural justice provides the answer through audi alteram partem. A fair hearing demands a genuine opportunity for the accused to understand and contest adverse material. In Natwar Singh v. Director of Enforcement,[7] the Supreme Court held that a hearing is illusory without disclosure of the materials relied upon. As Mayson argues,[8] procedural fairness depends on the integrity of the process, not merely its outcome. Algorithmic transparency is simply not a matter of technological preference, but a strict constitutional necessity.
ALGORITHMIC BIAS ROOTED IN HISTORICAL DATA
Secrecy is not the sole concern surrounding algorithmic bias. Historical data is also a prime contributor. Data such as arrest records, conviction status, and past policing practices often reflect long-standing social and economic disparities. When algorithms are trained on historical data sets[9], they may carry forward inequalities instead of judging the individual neutrally.
Attributing such bias exclusively to the algorithms would present an incomplete picture. Judges and other decision makers can also be affected by unconscious bias, inconsistency, and outside influences. However, unlike human intuition, these algorithmic systems can be re-calibrated whenever discriminatory patterns emerge.
As Barocas, Hardt, and Narayanan observe, the central challenge is not whether bias exists, but whether institutions possess effective mechanisms to identify, measure, and reduce it. Under Article 14, algorithms rely on historical data that unfairly targets marginalized communities without a rational nexus. In Shayara Bano v. Union of India,[10] the Supreme Court held that arbitrary actions violate the right to equality because the accused is denied any meaningful way to inspect or challenge opaque risk scores, and using them to decide bail is fundamentally arbitrary.
Under Article 21, personal liberty demands a real, fair decision by a judge, not automated calculations. In Babu Singh v. State of U.P.,[11] the Court ruled that refusing bail requires reasoned judicial discretion, not mechanical rules. Judicial independence is compromised when an algorithmic score gradually becomes the conclusive factor in determining bail.
Berk and his colleagues warned[12] that predictive models should remain as an advisory tool rather than being substitutes for judicial reasoning. Therefore, judges must continue to evaluate every application individually, both the algorithmic output and broader circumstances of the accused, and assess it independently. Only then does technology enhance, rather than degrade the administration of justice.
BUILDING A CONSTITUTIONALLY SOUND FRAMEWORK
The increasing integration of artificial intelligence into bail adjudication explains how technology is no longer peripheral to criminal justice and is an integral part of the framework. The aim is not to prohibit algorithmic decision-making but to regulate it within constitutional limits. Such an objective can only be achieved with fairness, transparency, and accountability.
All of these safeguards rest on procedural due process, natural justice (audi alteram partem), and the duty to deliver reasoned decisions. Blind judicial reliance on the results of computer-generated algorithms violates the right to a fair hearing and treats unvalidated output as inadmissible, untested scientific evidence.
Generally, courts enforce this by updating procedural laws to mandate standardized algorithmic summary sheets that disclose error rates, key inputs, and validation tests in each bail file. A standing judicial panel helps keep these tools accountable without giving away trade secrets. They work in coordination with the public, implementing rules that require bias checks before a tool is adopted, along with confidential court orders that let experts inspect the code privately.
These predictive systems are only as reliable as the information on which they are trained. Administrative mistakes in previous histories and incomplete criminal records may substantially affect predictions and produce unjust outcomes. As Chouldechova demonstrates[13], even statistically sophisticated prediction models may generate adverse impacts unless the design and validation are examined periodically.
Proper judicial education represents another essential reform. Judges, prosecutors, and defence counsel should undergo comprehensive training in algorithmic reasoning, automation bias, and statistical interpretation. Such knowledge enables courts to critically evaluate predictive recommendations instead of accepting them blindly or rejecting them ignorantly. Technology should inform legal reasoning, not redefine it.
CONCLUSION
Using artificial intelligence to decide bail threatens the core idea of fairness. While computers are known for speed and consistency, statistical probability cannot replace individualized scrutiny. Deciding whether an accused person remains in prison involves common sense, human empathy, and an understanding of real-life context, which software lacks.
Courts often let machines trained on past biases guide their rulings. While efficiency helps court administration, it is not the actual purpose of justice. Artificial intelligence is useful for sorting and organizing files. Yet judges continue to carry full responsibility for custody decisions. An accused person’s right to be presumed innocent must not be compromised by substituting human judgement with technology.
Author(s) Name: Akshay N Mahajan (R.L Law College, Belagavi)
References:
[1] Robert Kehl, Priscilla Guo and Samuel Kessler, Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments in Sentencing (Harvard Law School 2017).
[2] Satender Kumar Antil v Central Bureau of Investigation (2022) 10 SCC 51.
[3] Constitution of India 1950, Art 21.
[4] Maneka Gandhi v Union of India (1978) 1 SCC 248.
[5] State v Loomis 881 NW 2d 749 (Wis 2016).
[6] Pankaj Bansal v Union of India (2024) 7 SCC 576.
[7] Natwar Singh v Director of Enforcement (2010) 13 SCC 255.
[8] Sandra G Mayson, Bias In, Bias Out, 128 Yale L.J. 2218 (2019).
[9] Solon Barocas, Moritz Hardt & Arvind Narayanan, Fairness and Machine Learning: Limitations and Opportunities (MIT Press 2023).
[10] Shayara Bano v Union of India (2017) 9 SCC 1.
[11] Babu Singh v State of U.P., (1978) 1 SCC 579.
[12] Richard Berk et al., Fairness in Criminal Justice Risk Assessments: The State of the Art, 56 Sociol. Methods & Res. 1 (2021).
[13] Alexandra Chouldechova, Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments, 5 Big Data 153 (2017).