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.

- 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 checkpoints. Consequently, public order and law enforcements 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.

(Answer in 250 words)
Introduction
Predictive policing has visibly improved public order, but the memorandum asks whether that gain was bought at the cost of fairness to one immigrant, low-income neighbourhood.

(a) Ethical issues and biases

- Inherited bias: The model learns from past records; where earlier policing was discriminatory, it returns that discrimination as objective prediction.
- Profiling and stigma: Marking one neighbourhood exposes every resident, the innocent included, to heavier patrolling, checkpoints and preventive detention.
- Privacy without consent: Biometric capture in a crowd takes data from people who are neither suspects nor volunteers.
- Opacity and due process: Residents cannot see or correct what is recorded against them, and preventive action rests on probability rather than evidence.
- Diffused accountability: Responsibility for a wrongful detention scatters between vendor, analyst and officer.

(b) The alternatives before me

- Continue unchanged: Preserves order, but treats a credible allegation of bias as noise and hardens the community's hostility.
- Suspend entirely: Ends the bias and satisfies the delegation, but discards a lawful investigative aid and risks the return of gang violence and drug trafficking.
- Retain under audit and safeguards: Keeps the security gain while subjecting the tool to independent scrutiny, human decision and a citizen's remedy; slower, costlier, and dependent on supervision I must personally guarantee.

(c) The action I would adopt

- I would adopt the third alternative: I would commission an independent audit of the data and of outcomes by neighbourhood; bar arrest or preventive detention founded on a prediction alone; restrict biometric collection and fix retention limits; open a route for residents to see, contest and correct their data; publish the operating standards; and seat the memorandum's authors on a panel reviewing outcomes periodically.

Conclusion
The system must support the officer's judgement, not replace it. Order bought by unequal suspicion is not good policing; audit, human decision and a citizen's remedy make the improvement defensible.