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.
What the examiner wants
Preserve legitimate public-order gains without perpetuating biased historical data, opaque surveillance or discriminatory targeting.
Demand-wise check
- 1Stakeholders: residents, victims of crime, police, Ravi, marginalized communities and wider public≈40 words
- 2Biases: historical, selection, measurement, proxy and feedback-loop bias≈40 words
- 3Issues: privacy, notice, due process, proportionality, explainability, equal treatment, chilling effect and accountability≈40 words
- 4Options: continue, suspend, narrow use, independent audit, human review, transparency/redress, data revalidation≈40 words
- 5Preferred path: audited, purpose-limited system with human authorization, contestability and community safeguards≈40 words
Open in about 30 words and close in about 30.
Answer plan
Where marks usually go
- Assuming better public order proves the model is unbiased
- Treating historical crime data as neutral ground truth
Value addition
- Bias feedback loop plus safeguard stack
→ Draw it in the body, next to the point it supports.
Verified value additions
These are verified official-source additions selected for this exact PYQ. Use only the point that strengthens your argument.
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