Exploring the Role of AI in Law Enforcement
Law Enforcement Agencies (LEAs) are increasingly utilizing Artificial Intelligence (AI) to enhance their operations, particularly through capabilities for predictive policing. This trend is indicative of a global shift towards the adoption of new technologies within law enforcement.
Global Adoption of AI in Law Enforcement
In the United States, the New York Police Department has embraced tools like Patternizr for pattern analysis and officer deployment. Meanwhile, in China, the government employs robots for crowd control and utilizes drones and detention cameras to monitor suspicious activities. Furthermore, scientists are working on a virtual reality model of Shanghai, which aims to provide real-time assistance to police and rescue services.
In both the US and Australia, AI technologies are increasingly focused on child protection. For instance, the employment of Clearview AI in America and the Australian Centre to Counter Child Exploitation enables more rapid threat detection and prevention in child exploitation cases. Additionally, South Korea has introduced AI patrolling cars that integrate voice recognition, video analysis, and real-time data processing to enhance road security.
Market Growth and Investment
The global market for predictive policing is projected to reach US $157 billion by 2034, with a compound annual growth rate (CAGR) of 46.7 percent during 2025-34. This rapid growth is driven by the potential to integrate vast criminal datasets, thereby expediting investigative processes, which is particularly appealing to governments, including India.
The police-to-population ratio in India currently stands at 153 per 100,000 people, which falls short of the 222 per 100,000 recommended by the United Nations. This disparity underscores the necessity for increased resource distribution and the integration of technology into law enforcement practices.
Applications of AI in Policing
The applications of AI in law enforcement extend from counterterrorism to crowd management. In Uttar Pradesh, AI-powered drones and CCTVs have proven effective for tracking individuals and managing traffic during large gatherings, such as the Kumbh Mela. Moreover, modern tools developed by central agencies like the Bureau of Police Research & Development (BPR&D) are exploring deep and dark web spaces to gauge sentiment and provide credible intelligence inputs to LEAs.
India is also making strides in combating cybercrimes, including online money laundering. The Enforcement Directorate employs advanced analytical AI/machine learning tools from the Financial Intelligence Unit (FIU) to detect suspicious monetary patterns and prevent the routing of unaccounted money in the form of Virtual Digital Assets.
Challenges and Concerns
Despite the promise of AI, there are challenges associated with its deployment. Instances where AI systems have faltered, such as the tragic events during the Rath Yatra in Puri, highlight the limitations of current technologies. Technical inconsistencies, including false positives—where individuals, particularly those with darker skin tones, are disproportionately targeted—raise significant concerns about algorithmic biases and the accountability of technology providers.
Governance Considerations in AI Deployment
The efforts of states to modernize their police forces are supported by initiatives such as the “Assistance to States & UTs for Modernisation of Police” (ASUMP), which allocates INR 4,846 crore over five years (2021-26). Certain states like Delhi and Tamil Nadu have adopted ‘Innsight’, an AI tool for data analysis developed by Innefu Labs, although it has faced scrutiny due to cyberattacks and data breaches.
Such cases underscore the necessity for a governance framework that establishes due diligence for private firms securing contracts within the police sector. The deployment of AI tools must be accompanied by mechanisms that ensure explainability and accountability to mitigate opaque behaviors.
Need for Regulatory Framework
A comprehensive governance framework for AI deployment in law enforcement must address issues of bias, discrimination, and liability. This framework should reconcile operational use with legal tests of legality, necessity, and proportionality, especially given the fragmented regulatory landscape surrounding biometric data.
Without such a framework, public trust in law enforcement technologies may diminish, particularly amidst concerns around social profiling.
Enhancing AI Integration and Oversight
To keep pace with technological advancements, LEAs must implement evaluation mechanisms alongside AI integration. Companies should undergo periodic algorithmic audits and comply with clear mechanisms to qualify for procurement by LEAs. Establishing an Artificial Intelligence Safety Institute to develop robust safety and ethical testing standards tailored for the Indian context could be a pivotal policy initiative.
Additionally, pilot programs should be mandatory to assess the real impact of AI tools against defined risk parameters. Creating an Incident Database to document risks and develop harm reduction mechanisms could aid in understanding evolving dangers associated with AI deployment.
Conclusion
In conclusion, while the integration of technology in law enforcement offers immense potential to enhance productivity and streamline processes for apprehending offenders, the responsibility for any mishaps during the implementation of these technologies rests with the operating agency. Thus, thoughtful regulation and human oversight are crucial for developing effective governance frameworks to ensure the safe and trusted deployment of AI systems in a diverse nation.
