Landmark Judgments On Predictive Policing And Algorithmic Fairness
What is Predictive Policing and Algorithmic Fairness
Predictive policing refers to using data analytics, algorithms, machine learning etc., to forecast where crimes are likely to occur, or who might be involved, so as to prevent crime or allocate police resources more efficiently.
Algorithmic fairness concerns how these systems are designed, how data bias is avoided or mitigated, transparency, accountability, non-discrimination, and ensuring that people affected by such algorithms have rights (e.g. to contest or question algorithmic decisions).
Legal issues that arise include: privacy, due process, equality before law, arbitrariness, right to know how decisions are made, ability to correct or review, ensuring that algorithms do not reflect or amplify historic bias (in policing, race, class, gender etc.).
Key Judgments and Cases
Here are several important cases that, while not always perfectly square with “predictive policing,” offer fairly strong analogues in terms of legal reasoning about automated systems, fairness, surveillance, discrimination, privacy and state power. I also include some U.S./Foreign law since India has limited precedents specifically on predictive policing.
1. Selvi v. State of Karnataka, (2010) 7 SCC 263 (India)
Facts:
The issue was whether involuntary administration of certain “scientific techniques” (like narcoanalysis, polygraph, brain mapping) as part of criminal investigations violates constitutional rights.
Judgment / Holding:
The Supreme Court held that involuntary administration of such techniques goes against Article 20(3) (protection against self-incrimination) and Article 21 (personal liberty, bodily autonomy) unless the person consents.
The court stressed the need for safeguards, procedural fairness, voluntariness, and oversight.
Relevance to Predictive Policing / Algorithmic Fairness:
If predictive policing tools use, e.g., behavioral profiling or algorithms that infer personal traits, there is potential overlap with “scientific techniques.”
The reasoning in Selvi establishes that state cannot deploy invasive or automated inference methods without procedural safeguards and respect for personal liberty and dignity.
2. Puttaswamy v. Union of India, (2017) 10 SCC 1 (Right to Privacy Case, India)
Facts:
Petitions challenging whether right to privacy is a fundamental right, challenges to surveillance, data collection, Aadhaar etc.
Judgment / Holding:
The Supreme Court unanimously held that the right to privacy is a fundamental right under Articles 14, 19, and 21.
The court laid down tests for state interference: legality, necessity, proportionality.
Emphasis on informational privacy, data protection, and dignity.
Relevance:
Predictive policing involves processing large volumes of data, possibly sensitive personal data, sometimes involving profiling, location, behavior etc.
The principles in Puttaswamy are directly applicable: any predictive policing scheme must have a legal basis, be necessary (e.g. justified to prevent crime), proportional (not overly broad or arbitrary), and must maintain safeguards including transparency, possibility to review etc.
3. K.S. Puttaswamy (Aadhaar II) or subsequent Aadhaar judgments
While the main Puttaswamy judgment already covers privacy, subsequent Aadhaar cases further clarify how biometric and personal data must be handled. E.g., courts insist on:
Informed consent
Purpose limitation (data collected only for specified purpose)
Data security and protection
These aspects help in algorithmic fairness, because fairness also requires that data used is collected lawfully and with suitable protections, minimizing risk of misuse or bias.
4. Justice K.S. Puttaswamy (Retd.) v. Union of India — supplemental, and cases on biometric data
Although we don't have a named case that directly rules on predictive policing, the decisions on Aadhaar, digital privacy, biometric data, etc., all provide legal standards that would apply to any use of predictive algorithms by the state.
5. Foreign Cases: Loomis v. Wisconsin (U.S.)
Facts:
In State v. Loomis (Wisconsin Supreme Court, later refused certiorari at U.S. SC), the defendant was sentenced using COMPAS, a risk assessment algorithm. He argued that use of the closed-source predictive risk tool (which included consideration of race etc.) violated his due process and equal protection rights.
Judgment / Holding:
The Wisconsin Supreme Court held that risk assessment tools can be used in sentencing, but with constraints: the court must inform the defendant, ensure that the tool is not the sole or determinative factor, that the defendant has rights to challenge it, and that there is transparency and validation of the tool.
Relevance:
This is a concrete case of algorithmic fairness in sentencing; many of the same concerns apply to predictive policing (which is earlier in the law enforcement process).
The reasoning indicates that courts expect algorithmic systems used by the state to be explainable, transparent, with guarding against bias, and that individuals have rights to understand and challenge outcomes.
6. United States v. Curry (4th Cir. 2020)
Facts:
Case concerned predictive policing systems and whether they undermine constitutional rights under the Fourth Amendment (search and seizure) and Due Process.
Judgment / Holding (at appellate level):
The court expressed concern that predictive policing systems, if used to stop, search, arrest based on algorithmic predictions without individualized suspicion, may violate the Fourth Amendment.
Emphasized that algorithmic “hunches” cannot replace the constitutional guarantee of reasonable suspicion.
Relevance:
Shows that algorithmic fairness demands the state use predictive tools in a way that does not bypass due process safeguards, does not lead to discrimination or arbitrary police powers based on opaque algorithms.
7. England / UK case: South Wales Police v. Various Cases (Using Facial Recognition)
In the UK, there have been decisions (e.g. by Court of Appeal or High Courts) in which use of facial recognition by police (which is part of predictive policing in a broader sense) has been challenged for fairness, privacy, equality.
One case referenced in legal commentary is South Wales Police, where Court of Appeal held that the use of live facial recognition technology by the police without sufficient regulatory safeguards may violate rights of privacy and equality. While not Supreme Court of India, it's judicial precedent important for comparative law and understanding algorithmic fairness.
Gaps and Principles Evolving in India (Based on Existing Cases and Legal Scholarship)
From the Indian cases and constitutional standards, though no direct Supreme Court decision fully treating predictive policing algorithms, the following principles can be deduced or are in play:
| Principle | Source / Case(s) | What it means for predictive policing & fairness |
|---|---|---|
| Right to Privacy | Puttaswamy (2017), Aadhaar cases | Predictive tools must respect informational privacy; collection and use of personal data must be legal, necessary, proportionate. |
| Due Process & Procedural Safeguards | Selvi case, as well as evidentiary norms | Individuals should have rights to challenge algorithmic assessments; usage should not override consent or rights. |
| Transparency & Explainability | Part of privacy jurisprudence, constitutional rights; some Indian scholarly commentary | Algorithms should not be “black‑boxes” when used by state; citizens should have access to reasons, understanding, possibility to contest outcomes. |
| Non‑discrimination / Equality | Article 14, 15; rights of marginalized groups; e.g., in other cases (LGBT, caste etc.) fairness in law is constitutional duty | Predictive systems must not replicate or reinforce biases (caste, religion, race, gender etc.). Training data should be checked, tools audited. |
| Accountability and Oversight | Judicial review, rules around scientific/automated techniques (Selvi), privacy cases | State bodies must be accountable for deploying tools; there should be oversight (judicial, legislative), reporting, remedy for wrong predictions. |
Potential Future Landmark Direction in Indian Jurisprudence
Given the rapid rise of predictive policing initiatives in various states (as referenced in academic literature), courts may in future decide cases that more directly test:
The legality of predictive policing tools under constitutional law (Articles 14, 19, 21).
Whether such tools violate privacy rights when using data profiling, location, social media etc.
Whether algorithmic decisions might be challenged in criminal or policing proceedings (e.g. misuse, false positives).
Whether there must be regulatory frameworks (statutes or rules) governing deployment, bias audit, transparency.
Summary: What We Know So Far and What Remains
What we know: Indian Supreme Court has strongly protected privacy, personal liberty, bodily autonomy, and has imposed constraints on automated or scientific techniques where they may violate constitutional rights. It has not yet fully dealt with predictive policing algorithms in a mature case law sense. Foreign jurisprudence provides useful examples of how courts balance algorithmic tools with fairness.
What remains untested / needed: Specific Supreme Court case in India holding that predictive policing algorithms are lawful/unlawful; standards for algorithmic fairness; obligations for transparency, audits; remedy for algorithmic harms; etc.
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