Ravi is a senior police officer with vast experience in riot control and cyber-policing. Since one year, he has been the Superintendent of Police (SP) of a district with a history of frequent rioting. Last year, Ravi had sought installation of an AI enabled software for predictive policing. This new system has been operational for approximately six months. This system employs advanced algorithms for capturing the biometric data of persons in a crowd and swiftly relating it to a data library. This has enabled the police to identify the persons involved in various crimes. The system has identified an immigrant and low-income neighbourhood as a centre for gang violence and drug trafficking. Aided by this AI analysis, the local police has focused its patrolling, preventive detentions and establishing checkposts. Consequently, public order and law enforcement has visibly improved. Last week, some community leaders, civil rights lawyers and human rights activists visited Ravi's office. They submitted a memorandum that the new system is faulty as it is based on incorrect historical data caused by social biases and discriminatory policing. The memorandum also alleges that the increased surveillance has created a climate of tension amongst residents. This feeling is aggravated by the fact that the residents are not aware of the data noted against their names. (a) What are the ethical issues including biases involved in the use of AI in data-driven policing? (b) Place yourself in Ravi's role and discuss the alternatives available. Justify the action that optimises compliance with ethics.
GS420 Marks2026Model answer
Introduction
Predictive policing promises efficiency, but Ravi confronts a clash between public order and the equal moral worth of a vulnerable community. Puttaswamy (2017) requires State surveillance to satisfy legality, necessity and proportionality — a test no algorithm can be allowed to outsource.
Value Addition: The Feedback-Loop Trap
(a) Ethical issues and biases in AI-driven policing
- Algorithmic bias — discriminatory arrest history is fed back as "risk"; garbage-in, garbage-out. Eg. COMPAS recidivism bias, USA.
- Privacy without consent — indiscriminate biometric capture of crowds; no notice or purpose limitation, contra DPDP Act, 2023.
- Opacity — residents unaware of data against their names; denies audi alteram partem and Article 14 non-arbitrariness.
- Guilt by locality — profiling an immigrant neighbourhood treats persons as means, not ends (Kant); collective stigma.
- Automation bias, diffused accountability — "the algorithm decided" erodes the officer's moral agency and answerability.
- Chilling effect — climate of fear deters lawful movement and assembly; burden falls on the least advantaged (Rawls).
(b) Alternatives before Ravi
| Alternative | Ethical merit | Ethical cost |
|---|---|---|
| Continue unchanged, citing improved order | Utilitarian gain in public safety | Entrenches bias; majoritarian tyranny |
| Scrap the system entirely | Ends discrimination at once | Forfeits genuine crime-control gains; public funds wasted |
| ★ Suspend targeting, audit, reform, engage | Order with dignity and due process | Slower results; demands political backing |
Justified course — calibrated correction
- Suspend AI-triggered preventive detentions on human-verified intelligence meanwhile.
- Independent algorithmic audit with NCRB/NIC and an ethics committee; publish error rates and bias findings.
- Right to know and rectify — grievance desk for residents to access and correct entries (DPDP principles).
- Human-in-the-loop — written justification by the SHO before any AI-prompted action; accountability restored.
- Community policing — mohalla committees and local recruitment to rebuild the trust deficit (2nd ARC, Public Order).
Conclusion
Technology must remain a tool of justice, not a substitute for it. Aligning the system with NITI Aayog's Responsible AI principles lets Ravi secure both Article 21 liberty and the constitutional promise of fraternity.
Word count 392Indicative model answer · for structured practice, not an official answer key.
Answer LengthModel answers may exceed the word limit for better clarity and depth. Use them as a guide, but always frame your final answer within the exam's prescribed limit.
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