SMART POLICING VS. FUNDAMENTAL RIGHTS: A LEGAL FRAMEWORK FOR AI-DRIVEN PUBLIC SAFETY AND PREDICTIVE POLICING UNDER THE VIKSIT BHARAT 2047 VISION

SMART POLICING VS. FUNDAMENTAL RIGHTS: A LEGAL FRAMEWORK FOR AIDRIVEN
PUBLIC SAFETY AND PREDICTIVE POLICING
UNDER THE VIKSIT BHARAT 2047 VISION
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SMART POLICING VS. FUNDAMENTAL RIGHTS: A LEGAL FRAMEWORK FOR AI-DRIVEN PUBLIC SAFETY AND PREDICTIVE POLICING UNDER THE VIKSIT BHARAT 2047 VISION
By Mansi Shekhawat
BBA. LLB (HONS.) 5TH YEAR JAIPUR NATIONAL UNIVERSITY
ABSTRACT
The rapid incorporation of Artificial Intelligence into policing, in line with India’s Viksit Bharat 2047 vision, presents an immediate legal crisis. The same technologies expected to enhance public safety, stand to encroach on constitutionally protected rights of privacy, equality, and due process. This article critically examines the legal concerns arising from AI-enabled smart policing, characterized by the implementation of predictive algorithms, facial recognition and mass surveillance technologies. Using a framework of Indian constitutional law, especially Puttaswamy v. Union of India (2017) and the evolving contours of the Digital Personal Data Protection Act, 2023, this article analyzes the extent to which existing frameworks are adequate to regulate algorithmic policing. This article provides a blueprint for legal remedies and proposed interventions including a proportionality-and-necessity test for deploying AI in policing, establishment of an independent auditing body and mechanisms for citizen grievances, with the central argument being that secured digital governance requires law enforcement to be as accountable to the constitution, as it is to the technology itself. Lawful policing must necessarily equate smart policing.
Index Terms – Smart Policing, Predictive Algorithms, Fundamental Rights, Data Governance, Evidentiary Admissibility.
1. INTRODUCTION
In contrast to India’s traditional policing methods-physical presence, human informants and reactive investigations-Viksit Bharat 2047 aims at creating a paradigm shift to a data-driven regime. The vision includes the integration of predictive policing platforms, AI-driven surveillance, facial recognition technologies (FRT), and crime mapping algorithms to shape public safety interventions. State governments such as Telangana (SHE Teams), Delhi (with 300,000 cameras deployed under the CCTNS scheme) and Tamil Nadu (through its CCTNS network) have already introduced preliminary forms of the technology. Globally, similar initiatives such as the U.S.’s PredPol and the UK’s National Data Analytics Solution underscore both the transformative promise and the inherent perils of algorithmic policing. While there is an inevitable push towards algorithmic policing in India, the pertinent question for Indian constitutional law does not lie in whether these technologies can be used, but rather under what constitutional framework their use is justifiable.
The implementation of AI in policing poses an immediate constitutional quandary. On one hand, the state is under constitutional duty to maintain public order under Articles 355 and
356 of the constitution, while on the other, every individual has the right to privacy (Article 21), equality (Article 14), and freedom from arbitrary executive action (Article 22). The Puttaswamy v. Union of India (2017) 10 SCC 1 recognized informational privacy as a fundamental right, which can only be restricted in accordance with the tripartite test of legality, necessity and proportionality. Without
SMART POLICING VS. FUNDAMENTAL RIGHTS: A LEGAL FRAMEWORK FOR AIDRIVEN
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appropriate legal regulations to govern its deployment, algorithmic policing tools are likely to fail all three dimensions of this test.
This article will analyze the constitutional challenges of AI policing in four sections: First, it will unpack the constitutional frailties created by algorithmic policing technologies; Second, it will critically examine how current and proposed data governance mechanisms address these concerns; Third, it will discuss challenges surrounding the admissibility of AI generated crime data as evidence; and finally, it will present a blueprint for lawful AI policing. This article does not seek to impede technological advancement but only to ensure that it is implemented within the constitutional bounds of the law.
2. ALGORITHMIC ARCHITECTURE VS. CONSTITUTIONAL GUARANTEES
2.1 The ‘Black Box’ Problem and Due Process
Predictive policing systems, ranging from logistic regression models to deep neural networks, operate as ‘black boxes.’ Their proprietary nature and complex design make them opaque and difficult to decipher. This opaqueness presents a fundamental conflict with the constitutional right to a fair trial under Article 21 and the principles of natural justice. In the absence of a mechanism for transparency, an individual surveilled, profiled, or detained based on an algorithmic risk assessment will be denied the most basic procedural protection of being informed about, and allowed to contest, an adverse decision by the state. In Maneka Gandhi v. Union of India (1978), the Supreme Court held that any procedure affecting personal liberty must be fair and reasonable.
Algorithmic tools, lacking mandated transparency, are unable to meet this standard. The European Union’s AI Act (2024) mandates a ‘meaningful explanation’ for high-risk AI outputs, a standard that is crucial for India to adopt.
2.2 Algorithmic Bias and Discrimination
AI systems learn from historical data. In the context of law enforcement in India, this includes a history of biased policing practices such as discriminatory arrests in Scheduled Caste and Scheduled Tribe populations, over-policing of certain communities and socio-economic profiling. Training predictive models on such data not only perpetuates these biases, but it also lends them a semblance of objective neutrality. Such actions may directly violate Article 15, which proscribes discrimination based on religion, race, caste, sex or place of birth and Article 14 which guarantees equal protection of laws. An AI tool that assigns differential risk scores to individuals based on their community, violates both provisions. The well-known U.S. COMPAS algorithm, found to be twice as likely to incorrectly flag Black defendants as future recidivists according to ProPublica analysis, offers a crucial cautionary tale for Indian policymakers.
2.3 Surveillance, Facial Recognition and the Right to Privacy
Despite the proliferation of the National Crime Records Bureau’s Automated Facial Recognition System (AFRS) across India, there is a conspicuous lack of specific legislation to govern its use. FRT specifically threatens privacy as it facilitates the mass, non-consensual and real-time surveillance of individuals in public spaces. The
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proportionality test mandated by the Puttaswamy ruling requires any restriction on the right to privacy to serve a legitimate state aim, adopt the least restrictive means and impose constraints proportionate to that aim. The generalized deployment of FRT in law enforcement, as opposed to targeting clearly defined and specific threats, fails the necessity and proportionality components of this test. Judicial interventions concerning FRT deployment in Unnao (2019) and challenges before the Madras High Court have questioned the constitutionality of the technology in the absence of supporting legislation.
3. REGULATORY AND DATA SECURITY FRAMEWORKS
3.1 Compliance with National Data Privacy Laws
The Digital Personal Data Protection Act, 2023 (DPDPA) serves as the primary legal framework governing data protection. However, the exemption under section 17(1)(a) which allows data processing for ‘the sovereignty, integrity, and security of India, or prevention, detection, investigation, and prosecution of any offence’, could be interpreted expansively, creating a black hole where law enforcement data processing is completely unregulated. Any constitutionally sound interpretation of the DPDP Act must necessitate that even exempt data processing meets the proportionality test. The constitutional safeguards desired by the Srikrishna committee to restrict unchecked state surveillance remain un addressed in the current law.
3.2 Data Minimization and Purpose Limitation
The collection and storage of massive amounts of data under smart policing systems (including surveillance camera footage, biometric scans, call logs, location data and social media interactions) necessitates robust data minimisation and purpose limitation principles. While the DPDPA recognizes data minimisation in law, its application to law enforcement agencies is conspicuously absent. A lawful policy ought to define: (a) permissible data retention periods in proportion to the severity of the offence investigated; (b) mandatory deletion timelines post the closure of an investigation or acquittal of an accused; and
(c) limitations on data sharing across departments without a judicial mandate.
3.3 Liability in Systemic Errors
In the event of an AI system generating a false positive resulting in an unlawful arrest, responsibility is diffused among the developer of the software, the state agency that purchased and deployed it and the officer who acted based on the tool’s findings. The Indian Penal Code and CrPC, 1973, are not designed for AI related harm. There should be a strict liability on the state agencies purchasing AI tools, along the lines of the absolute liability doctrine in M.C. Mehta v. Union of India (1987). Mandatory indemnity clauses in the Government Contracts to procure AI tools are needed. The differentiation between an officer acting in good faith based on an algorithm and one abusing the tool should be maintained, but not at the cost of absolving the state of responsibility.
4. EVIDENTIARY CHALLENGES AND JUDICIAL SCRUTINY
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The preliminary issue in any criminal procedure would be whether an AI derived risk assessment or a predictive alert would be considered reasonable suspicion to perform a stop-and-search and probable cause to effect an arrest under section 41 of the CrPC. There is no mention of this in the CrPC or the BNSS. American jurisprudence on the Fourth Amendment provides guidance in this regard. In United States v. Cortes-Martell (2018), courts established that predictions derived from algorithms cannot themselves be the basis for probable cause. These can be used to corroborate findings based on independent human observation. Indian courts would do well to implement such requirements for corroboration. The AI evidence may form one part of many pieces of evidence but it would fail to meet the constitutional and procedural threshold in a search or an arrest by itself.
Under section 45 of the Indian Evidence Act (now the Bharatiya Sakshya Adhiniyam, 2023), the court must refer to the opinions of experts. In order for AI Forensic evidence to be admissible, the courts must set standards on par with the American Daubert standard for forensic evidence used in US federal courts; (i) The underlying scientific theory or technique must have been peer reviewed and subjected to scientific validation; (ii) The error rate of the algorithm must be known and divulged; (iii) The model used should have been independently audited. This ability and infrastructure does not currently reside in the Forensic Science Laboratories responsible for the validation of AI tools. It is necessary that a statutory National AI Forensic Standards Authority will certify all AI tools used for judicial purposes.
5. A LEGAL ROADMAP FOR SECURE SMART POLICING
Before deploying any AI policing tools, an Impact Assessment of its deployment on Fundamental Rights should be carried out, in the vein of an EU Data Protection Impact Assessment conducted under the GDPR. This FRIA would outline and analyze an application of the four-pronged test of proportionality: (i) legitimate objective- is the tool being used to advance a constitutionally protected law enforcement interest? (ii) suitability- is there empirical evidence that suggests the tool contributes towards achieving that objective?
(iii) necessity- can less rights-infringing means be used to achieve the stated objective? (iv) proportionality stricto sensu- do the anticipated benefits for public safety outweigh the erosion of fundamental rights? In case of FRT for crowd surveillance, it will most likely fail parts (iii) and (iv) while a targeted FRT for identified missing persons may succeed through all stages.
This paper advocates for the creation of a statuary National AI Policing Oversight Authority (NAIPOA) under the proposed Smart Policing (Regulation and Accountability) Act. The functions of the NAIPOA should include: the certification of all AI policing tools before use, an ongoing adversarial audit of deployed AI systems for bias, errors, mission creep and inaccuracy, the power to impose legally binding corrective actions, and a registry of all AI tools used by both state and central law enforcement agencies. Crucially, the NAIPOA should not be accountable to either the Ministry of Home Affairs or the Ministry of Electronics and Information Technology, but instead be a
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commission of judges, civil liberties experts and technical scholars.
The final pillar for a safe smart state is citizens’ access to redressal of any harm done to them. There are three interlinking means proposed for such a right: (a) a statuary right to explanation-an individual must be able to receive an explanation of AI driven profiling, surveillance or harmful law enforcement actions, based on AI findings, in terms intelligible to them, upon demand; (b) a complaints tribunal that independently examines complaints against AI tools with the power to grant damages, order expungement of data, and recommend prosecution; (c) annual reporting to parliament on the status of all AI policing tool deployments in the country. These three mechanisms are to ensure that the right to privacy, upheld in the judgment of Puttaswamy, and the remedies available underArticles 32 and 226 are effectively enforced.
6. CONCLUSION
The aspiration for Viksit Bharat 2047 necessarily involves the upgrading of our law enforcement apparatus. But it is also history that, left unchecked, the state’s machinery for security is easily transformed into the state’s machinery for oppression. We have examined in the above paragraphs how AI policing tools, in their present conception and partially in deployment, have the potential to create structural vulnerabilities in the areas of constitutional rights, data governance, evidentiary standards and allocation of liability. A regulatory framework encompassing a FRIA, statuary oversight authority, corroboration of AI leads and mechanism of redress is not an impediment to smart policing, it is a pre-
requisite for it. Digital governance cannot come at the expense of legal accountability and the rule of law. Smart statecraft is not about the prevalence of surveillance infrastructure; it is about the robustness of rights which that infrastructure is supposed to protect.
ACKNOWLEDGEMENTS
The authors are grateful to several legal scholars, civil liberties activists and technologists whose previous work on AI governance and fundamental rights have influenced the approach adopted here.
REFERENCES
[1] K.S. Puttaswamy v. Union of India, (2017) 10 SCC 1 (Supreme Court of India).
[2] Maneka Gandhi v. Union of India, (1978) 1
SCC 248.
[3] M.C. Mehta v. Union of India, (1987) 1 SCC 395.
[4] Digital Personal Data Protection Act, 2023, No. 22, Acts of Parliament (India).
[5] Bharatiya Nagarik Suraksha Sanhita, 2023, No. 46, Acts of Parliament (India).
[6] Bharatiya Sakshya Adhiniyam, 2023, No. 47, Acts of Parliament (India).
[7] European Parliament and Council, Regulation (EU) 2024/1689 (EU AI Act), Official Journal of the European Union, 2024.
[8] J. Angwin et al., ‘Machine Bias,’ ProPublica, May 23, 2016.
[9] B.N. Srikrishna Committee, ‘A Free and Fair Digital Economy: Protecting Privacy, Empowering Indians,’ Ministry of Electronics and Information Technology, 2018.
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[10] R. Richardson, J. Schultz & K. Crawford, ‘Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice,’ NYU Law Review Online, vol. 94, 2019.
[11] Government of India, ‘Viksit Bharat 2047: Voice of Youth,’ NITI Aayog, 2023.
[12] United States v. Cortes-Mrtell, Criminal No. 16-394, E.D. Pa. (2018).
[13] Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).
[14] National Crime Records Bureau, ‘Automated Facial Recognition System (AFRS): Project Document,’ Ministry of Home Affairs, 2019.