Integrating AI in Police Operations

Integrating AI in Police Operations

Context

In January 2026, India’s law enforcement entered a decisive phase of technological transition. The Delhi Police operationalised its Safe City Project, while Maharashtra began the statewide rollout of MahaCrime OS AI. Together, these initiatives reflect a shift from conventional policing to algorithm-driven governance, raising a fundamental dilemma: how to enhance public safety without undermining civil liberties.

About the Developments

1. Delhi: Safe City Project

Delhi’s Safe City Project integrates nearly 10,000 AI-enabled cameras equipped with:

• Facial recognition systems
   
• Distress detection tools to identify screams, emergency gestures, and unusual crowd density
   

This marks a move towards automated surveillance where real-time monitoring becomes continuous and preventive.

2. Maharashtra: MahaCrime OS AI

Maharashtra’s MahaCrime OS AI is designed as a statewide intelligence and investigation platform focusing on:

• Predictive policing (risk forecasting and hotspot mapping)
   
• Cybercrime investigation support
   

Developed with private collaboration, it uses AI copilots to analyse case files, detect patterns, and even generate investigation plans.

3. Surveillance Drones

AI-powered drones are increasingly deployed for:

• Crowd control
   
• Traffic monitoring
   
• Public order management
   

They create a “high-altitude surveillance advantage,” reducing reliance on ground personnel while amplifying observational capacity.

4. Data Backends: CCTNS-based Training

A major driver behind AI policing is the availability of long-term criminal datasets. Modern AI systems are trained using decades of records from the Criminal Tracking Network and Systems (CCTNS), enabling quicker pattern recognition and cold-case linkages.

Ethical and Administrative Concerns

Key Challenges in the 2026 Landscape

1. The “Black Box” Problem

Unlike written police manuals, AI systems rarely provide a transparent rulebook explaining why a person was flagged. This makes it extremely difficult for citizens to:

• challenge AI-assisted detention,
   
• demand accountability, or
   
• contest algorithmic decisions in court.
   

2. Accuracy vs. Harm

AI tools are only as reliable as their data and conditions. Errors from:

• poor-quality CCTV footage,
   
• biased training datasets, or
   
• flawed facial recognition
   can lead to wrongful detention and irreversible harm, including extreme outcomes in custodial settings.
   

3. Legal Vacuum and Weak Safeguards

Although the Digital Personal Data Protection Act (DPDPA), 2023 exists, broad exemptions for the state on grounds like “security” create major gaps in protection against surveillance misuse and unchecked profiling.

4. Presumption of Guilt

Predictive policing changes the logic of justice. Instead of investigating crime after evidence emerges, the system increasingly screens behaviour as suspicious before any wrongdoing is proven, weakening the constitutional principle of innocent until proven guilty.

Way Forward

1. Statutory Framework for AI Policing

India needs a dedicated legal framework that ensures:

• mandatory safety testing before deployment,
   
• transparency standards, and
   
• disclosure of decision-making logic where fundamental rights are affected.
   

2. Human-in-the-Loop Accountability

AI must remain assistive, not authoritative.

• Arrests, detention, and coercive decisions must always require human approval
   
• The responsible officer must remain legally accountable for the final action
   

3. Independent Algorithmic Audits

AI tools used in policing must undergo frequent audits to detect and eliminate:

• caste bias
   
• religious profiling
   
• gender bias
   
• locality-based stereotyping
   

Audits should be conducted by independent third-party institutions, not only internal agencies.

4. Reform Existing Legal Provisions

Legal reforms must ensure proportionality in data collection, especially biometric data. Laws such as the Criminal Procedure (Identification) Act, 2022 require stronger safeguards so that:

• non-convict biometric data is not collected indiscriminately, and
   
• surveillance remains necessary, limited, and justified.
   

Conclusion

Technology can strengthen policing capacity, but it cannot replace constitutional discipline. If unchecked, AI-driven policing risks turning governance into digital authoritarianism through mass monitoring, biased profiling, and invisible decision-making. A genuinely safe society is secured not by total surveillance, but by trust, transparency, proportionality, and the Rule of Law, ensuring that innovation remains anchored in constitutional values and democratic accountability.