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 system has been operational for approximately six months. This new 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.

Ethics
Ethics: Case Study
2026
20 Marks

This case study highlights the conflict between Technological Efficiency and Social Justice in the context of predictive policing. It underscores the tension between the state's duty to maintain Public Order and the preservation of Fundamental Rights (Article 14 and 21) against algorithmic bias.

Key Stakeholders in Ethical and Technology-Driven Policing

Key Stakeholders in Ethical and Technology-Driven Policing

a) Ethical issues, including biases, involved in the use of AI in data-driven policing

  • Algorithmic Bias and Discrimination: AI systems often rely on historical crime data which may reflect systemic prejudices. If past policing was discriminatory, the AI automates this, violating Article 14 (Right to Equality).

  • Feedback Loop of Surveillance: The system identifies specific areas for patrolling, leading to more arrests there, which further "confirms" the AI's prediction, creating a self-fulfilling prophecy of criminality in low-income clusters.

  • Transparency vs. Secrecy: The "Black Box" nature of AI prevents residents from knowing why they are flagged, infringing upon the Principles of Natural Justice and the Right to Information.

  • Privacy vs. Security: Mass biometric capture without specific suspicion challenges the Puttaswamy Judgment (Right to Privacy) and lacks a specific regulatory framework in India as of 2026.

  • Utilitarianism vs. Deontology: While the system provides the "greatest good" by reducing riots, it treats residents of specific neighborhoods as "means to an end," violating their inherent dignity.

b) Alternatives available and Justification for the most appropriate action

Option 1: Continue the current AI-led policing without changes.

ProsCons
Maintains immediate public order and suppresses gang violence.Risks permanent alienation of the community and legal challenges.
High efficiency in identifying known criminals in crowds.Violates Digital Personal Data Protection Act (DPDPA) 2023 principles of purpose limitation.

Option 2: Immediate suspension of the AI system.

ProsCons
Restores trust with activists and reduces tension in the neighborhood.Potential resurgence of rioting and loss of a valuable crime-fighting tool.
Upholds the precautionary principle regarding human rights.Wastes public exchequer funds invested in the technology.

Option 3: Integrated approach with Algorithmic Auditing and Community Policing.

ProsCons
Ensures compliance with the Supreme Court's August 2026 ruling on limited FRS use.Slower implementation due to bureaucratic and technical reviews.
Combines technological precision with human empathy and oversight.Requires additional training for the police force in AI ethics.

Justification for the most appropriate action:

I will adopt Option 3. As a senior officer, Ravi must balance the Rule of Law with Social Equity.

  • Immediate Step: Conduct an independent Algorithmic Audit to identify data biases, ensuring the AI distinguishes between "high-crime history" and "high-density patrolling history."

  • Transparency: In line with DPDP Rules 2025, establish a grievance cell where residents can inquire if they are on a "watch-list" and provide a mechanism for correction.

  • Human Oversight: Implement a "Human-in-the-loop" protocol; no detention will be based solely on AI flags without corroborating physical evidence (BNS provisions).

  • Community Engagement: Launch the Prahari scheme to involve community leaders in neighborhood safety, transforming the police image from "invaders" to "protectors."

    • Eg: The Delhi Police's recent shift toward using FRS only for specific criminal databases rather than mass assemblies serves as a precedent for targeted surveillance.

The ultimate goal of policing is to foster a sense of security, not just the absence of crime. By tempering technology with compassion and accountability, Ravi can ensure that "justice is not only done but seen to be done," upholding the constitutional vision of a Socialist and Secular Democracy.

Answer Length

Model 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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