Artificial Intelligence (AI) in Law Enforcement

Indian police forces are increasingly integrating Artificial Intelligence (AI) into their operations, e.g., Delhi Police’s Safe City Project.

• AI is reshaping surveillance, investigation, predictive policing, and forensic analysis.

Key Applications of AI in Law Enforcement

• Facial Recognition Technology (FRT)
  • Used to identify criminals, missing persons, and suspects from CCTV and image databases.
  • Capable of identifying individuals even in large crowds (e.g., stadiums, public events)
• AI-Enabled Surveillance Systems: 
  • Analyze live and recorded CCTV footage to detect suspicious behaviour, weapons, traffic violations, and accidents.
  • Object recognition allows law enforcement agencies to track vehicles or individuals across locations.
  • Drone surveillance enhances crowd management and search-and-rescue operations.
• Predictive Policing: 
  • Uses historical crime data to Identify crime hotspots, Predict types of crimes, potential offenders, and likely victims.
  • Potential utility in crime prevention, including emerging areas like elder abuse.
• Use of Robots in Policing:
  • Robots assist in Surveillance and patrolling, High-risk operations such as bomb disposal and hazardous area entry.
  • For instance Dubai’s street robots transmit real-time data back.
• Detection of Non-Violent Crimes:
  • AI assists in identifying Financial fraud, money laundering, counterfeit currency and goods.
• Pre-Trial Release and Parole Decisions
  • AI is used in the criminal justice system during the pre-trial phase and to determine the terms of parole for an offender. 
  • For instance, the US uses COMPAS (abbreviated for Correctional Offender Management Profiling for Alternative Sanctions) for offender risk profiling. 

Drivers of AI Use in Law Enforcement

• Rising Crime Complexity: Cybercrime, terrorism, financial crimes, and organised crime require data-intensive tools.
• Resource Constraints: India’s police-to-population ratio (153 per 100,000) is significantly below the UN norm (222), necessitating efficiency-enhancing technologies.
• Urbanisation and Crowd Management: Large urban populations, mass gatherings, and mega events increase surveillance and public safety challenges.
• Market Expansion: The global predictive policing market is projected to reach US$157 billion by 2034, indicating growing governmental reliance on AI-driven law enforcement tools.

AI Applications in Indian Law Enforcement

• Delhi Safe City Project: Installation of 10,000 AI-enabled CCTV cameras equipped with facial recognition systems and distress detection technologies to identify emergency-related sounds and facial expressions.
• MahaCrime OS AI (Maharashtra): An AI-powered investigation platform aimed at faster complaint processing, complex data analysis and efficient adherence to investigative procedures.
• Criminal Forensics: AI systems are trained on decades of criminal data from the Criminal Tracking Network and Systems, enabling pattern recognition and predictive analysis.
  • For Instance, Digitisation and preservation of fingerprints with higher accuracy.
• Cyber and Dark Web Monitoring: Tools developed by Bureau of Police Research & Development (BPR&D) analyse deep and dark web data for intelligence generation.
• Financial Crime Detection: Enforcement Directorate leverages AI/ML tools of FIU to detect suspicious transactions, mule accounts, and virtual digital asset laundering.
• Other State Police Deployments:
  • Uttar Pradesh: Trinetra app for criminal tracking
  • Delhi: Crime Mapping, Analytics & Predictive System (CMAPS) for hotspot identification

Limitations and Risks of AI in Policing

• Algorithmic Bias and Discrimination: AI systems are trained on historical crime data, which often reflects existing social and institutional biases.
• Erosion of Equality and Fair Trial Rights: Biased AI outcomes undermine the principle of equality before law and the presumption of innocence.
  • The use of proprietary “black-box” algorithms prevents accused persons from understanding or challenging AI-based decisions, violating the right to a fair trial.
• Surveillance and Loss of Privacy: AI-based facial recognition enables mass surveillance, continuous tracking, and profiling of individuals without their consent.
• Over-reliance on Automation: Generative AI-based police reports may overlook legal nuances, contextual factors, and ground realities.
  • Risk of technological determinism, where algorithmic outputs are treated as objective truth.
• Accountability and Governance Gaps:  There is no statutory AI rulebook or policing manual governing AI use, unlike traditional Police Manuals.
  • Opaque AI systems reduce transparency and accountability.

Way Forward

• Legal and Institutional Safeguards
  • Governments must mandate human rights impact assessments before procuring or deploying AI systems in policing.
  • A robust data protection law is essential to regulate biometric data collection, storage, and use.
  • AI deployment must satisfy tests of legality, necessity, and proportionality.
• Human Oversight and Accountability
  • AI systems must function strictly as decision-support tools and not replace human judgment in policing or judicial processes.
  • Clear accountability mechanisms must identify responsibility for errors, misuse, or rights violations caused by AI systems.
• Transparency and Explainability:
  • Law enforcement agencies must ensure transparency in AI decision-making so that affected individuals can understand and challenge outcomes.
  • Independent audits and algorithmic testing should be conducted regularly to identify bias and errors.
• Capacity Building and AI Literacy:
  • Police personnel and judicial officers must be trained in ethical AI use, limitations, and potential harms.
  • Public awareness of AI deployment in policing is necessary to build trust and democratic accountability
• Adaptive and Context-Specific Deployment:
  • Pilot-based deployment and context-specific trials before large-scale adoption.
  • Creation of an incident database to document AI-related harms and enable adaptive risk mitigation strategies.

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