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.
What are the ethical issues including biases involved in the use of AI in data-driven policing?
Central Dilemma: Public order and technological efficiency vs privacy, equality and non-discrimination.
(a) Ethical Issues and Biases:
- Algorithmic bias: AI trained on historical policing data inherits past prejudices — over-policing of minority/low-income areas creates a feedback loop (more surveillance → more arrests → more "data" confirming criminality).
- Discrimination: Article 14, 15, 21 violation — targeting a community based on ethnicity/income breaches equality and dignity.
- Presumption of innocence reversed: Predictive detention criminalises identity, not conduct.
- Privacy violation (Puttaswamy, 2017): Mass biometric harvesting without legal basis or consent fails necessity-proportionality test.
- Opacity and lack of due process: Residents cannot see or contest data against them — violates natural justice.
- Chilling effect on free movement, assembly, association.
- Accountability vacuum: Who is responsible for algorithmic errors — vendor, officer, or state?
Answered By:
Sakshi Negi
Sakshi Negi is a Delhi-based journalist and content specialist with expertise in educational content and digital media, focusing particularly on government exam preparation and career guidance. With a degree in Journalism and Mass Communication from NRAI School of Mass Communication and her background as a Kendriya Vidyalaya alumna, she brings unique insights into the Indian education system and government sector opportunities.
Important Questions from GS4
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