ARTIFICIAL INTELLIGENCE DRIVEN PREDICTIVE POLICING TOOLS: RESHAPING LAW ENFORCEMENT PRACTICES BY - RITUL ARYAN

ARTIFICIAL INTELLIGENCE DRIVEN PREDICTIVE POLICING TOOLS: RESHAPING LAW ENFORCEMENT PRACTICES
 
AUTHORED BY - RITUL ARYAN
 
 
 
ABSTRACT
The increasing integration of Artificial Intelligence (AI) into law enforcement has led to the rise of predictive policing tools, which use algorithms and historical crime data to forecast criminal activity and assist in resource allocation. These tools aim to enhance crime prevention by identifying high-risk areas and individuals, allowing for more proactive policing strategies. However, the implementation of AI-driven predictive policing raises significant ethical, legal, and social concerns. One of the primary issues is the potential for biased algorithms, which may reinforce systemic inequalities in the criminal justice system, particularly concerning race and socioeconomic status. Since these tools often rely on historical crime data, they can perpetuate patterns of over-policing in marginalized communities, leading to discriminatory outcomes.
 
In addition to bias, predictive policing raises questions about privacy and the extent to which surveillance tools infringe on civil liberties. The opacity of some AI systems further complicates accountability, as many algorithms operate as “black boxes,” making it difficult for law enforcement agencies to understand how predictions are made or to explain decisions to the public. The paper critically evaluates the advantages of predictive policing, such as improved efficiency in resource allocation, alongside the ethical challenges it presents. It also examines legal frameworks governing AI in law enforcement, exploring the need for transparent and equitable regulations to protect citizens' rights while ensuring that law enforcement practices remain effective. By analyzing real-world case studies and proposing regulatory solutions, this paper aims to provide a balanced perspective on how predictive policing reshapes law enforcement practices and its broader societal implications.
 
 
INTRODUCTION
In recent years, law enforcement agencies across the globe have increasingly turned to technology to enhance their capabilities in preventing and responding to crime. One of the most notable innovations in this space is the use of Artificial Intelligence (AI)-driven predictive policing tools. These systems leverage advanced algorithms, machine learning models, and big data analytics to predict where crimes are likely to occur, who might be involved, and how law enforcement resources can be allocated most effectively. By using historical crime data, geographical patterns, and sometimes even socio-economic factors, predictive policing aims to reduce crime by enabling law enforcement agencies to act proactively, rather than reactively.
The promise of predictive policing lies in its ability to optimize resources and improve public safety. Police departments in cities like Los Angeles, Chicago, and London have employed predictive policing tools to identify "hotspots" for crime or even forecast the likelihood that certain individuals may commit crimes in the future. In theory, these tools allow law enforcement to focus their efforts on high-risk areas or individuals, deploying officers more strategically, preventing crimes before they occur, and improving overall efficiency in policing.[1]
 
However, the integration of AI into policing is not without significant controversy and concern. One of the most pressing issues is the potential for inherent biases within these systems. Predictive policing algorithms often rely on historical crime data, which can be influenced by existing systemic biases in the justice system, such as racial profiling or over-policing in certain communities. This raises concerns that predictive tools may perpetuate discriminatory practices, disproportionately targeting marginalized groups and reinforcing inequalities in the criminal justice system.
 
Additionally, the use of AI in policing also raises critical privacy issues. The widespread use of surveillance technologies and data collection can lead to overreach and violations of citizens' rights. As these tools become more advanced, questions regarding transparency and accountability also arise, particularly in terms of how decisions are made by algorithms that may operate as “black boxes,” making it difficult for law enforcement or the public to understand how predictions are generated or to challenge them when necessary.
 
This paper explores the evolving role of AI-driven predictive policing tools, examining both the potential benefits and the ethical,[2] legal, and social challenges associated with their use in modern law enforcement. It aims to provide a comprehensive overview of how these tools are reshaping law enforcement practices and highlight the need for careful regulation and oversight to ensure fairness, transparency, and accountability.
 
OVERVIEW OF PREDICTIVE POLICING TOOLS
Predictive policing refers to the use of advanced data analytics, machine learning algorithms, and artificial intelligence (AI) to forecast where and when crimes are likely to occur, which individuals may be involved in criminal activities, and how law enforcement resources can be optimized. These tools are designed to help law enforcement agencies be more proactive in crime prevention by identifying potential threats before they happen. Predictive policing systems rely on historical crime data, geographic information, and other variables to generate predictions that guide policing strategies.[3]
 
There are several key types of predictive policing tools, each focusing on different aspects of crime prediction and prevention:
  1. Hotspot Policing: Hotspot policing is one of the most common predictive policing methods. It identifies geographic areas or neighborhoods that have a high likelihood of criminal activity based on historical data. By analyzing patterns of past crimes, AI models can pinpoint areas that may be at greater risk for incidents such as burglaries, assaults, or robberies. Police departments can then direct patrols and resources to these areas, with the goal of preventing crime through increased visibility and deterrence. The success of hotspot policing hinges on the accuracy of the crime data and the algorithms that identify high-risk areas.
  2. Risk Terrain Modeling (RTM): Risk Terrain Modeling takes a slightly different approach by considering environmental factors and the built environment when predicting crime. This model looks at the physical attributes of neighborhoods—such as the presence of bars, liquor stores, or vacant properties—and uses these factors to assess where crimes are more likely to occur. RTM helps law enforcement agencies understand how the landscape itself influences criminal behavior, offering insights into where crimes are likely to be concentrated based on environmental conditions.
  3. Risk Assessment Tools for Individuals: Predictive policing tools can also be used to assess the risk of individuals committing crimes in the future. These risk assessments are based on various factors, including criminal history, demographic data, and other behavioral indicators. Some tools are designed to predict recidivism rates for offenders or identify individuals who may be at risk of becoming involved in criminal activities. These assessments can help inform parole decisions or other law enforcement actions. However, they are often controversial, as they raise concerns about profiling and fairness.[4]
  4. Pattern Recognition and Social Network Analysis: Some predictive policing tools focus on recognizing crime patterns across time and space. These systems use machine learning to analyze complex datasets and identify correlations that might not be immediately obvious to human analysts. By recognizing these patterns, law enforcement can anticipate crime sprees, identify emerging trends, and intervene before criminal activities escalate. Social network analysis, in particular, allows predictive tools to identify relationships and connections between individuals, enabling police to detect potential criminal networks or gangs.
 
While predictive policing tools offer promising benefits in terms of improving efficiency, reducing crime rates, and enhancing resource allocation, they are not without their criticisms. The accuracy of predictions depends heavily on the quality and bias of the data used. If the data reflects historical biases—such as over-policing in minority communities—there is a risk that predictive policing tools may exacerbate existing inequalities in the criminal justice system. Furthermore, the use of these tools raises significant concerns about privacy and civil liberties, especially when surveillance data and personal information are involved.[5]
 
Thus, while predictive policing tools are reshaping modern law enforcement practices, their implementation requires careful consideration and oversight to ensure that they are used ethically, transparently, and equitably.
 
APPLICATIONS AND BENEFITS OF AI IN POLICING
Artificial Intelligence (AI) has significantly transformed modern law enforcement practices, offering new opportunities for improving public safety, enhancing operational efficiency, and preventing crime. AI-driven tools are now integral to a wide range of policing activities, from crime prediction to real-time surveillance. These technologies offer substantial benefits, but their applications also require careful attention to avoid potential ethical and social issues.[6]
  1. Crime Prediction and Prevention: One of the most prominent applications of AI in policing is predictive policing, which uses historical crime data, machine learning algorithms, and other data sources to forecast criminal activity. AI tools can identify patterns in past crimes and predict where and when future crimes are likely to occur. For instance, predictive policing systems can help law enforcement agencies identify high-risk neighborhoods or times when crimes, such as burglaries or robberies, are more likely to take place. This enables police departments to allocate resources more effectively, concentrating patrols in areas where crimes are predicted to happen, which can help prevent incidents before they occur.
  2. Optimizing Resource Allocation: AI applications also help optimize the allocation of law enforcement resources. By analyzing large amounts of data, AI tools can assist in determining where police presence is needed most and which officers or units should be deployed to specific locations. This data-driven approach ensures that resources are used efficiently and can improve response times during emergencies. Furthermore, AI tools can help track and manage police officers' shifts, vehicles, and equipment, leading to more streamlined and cost-effective operations.
  3. Real-Time Surveillance and Monitoring: AI has revolutionized surveillance by enhancing real-time monitoring capabilities. Advanced AI-powered surveillance systems, such as facial recognition software and intelligent video analysis, can assist in identifying suspects, tracking criminal activity, and even preventing crimes in progress. For example, AI can analyze video footage from public spaces or private security cameras, detect unusual behavior or potential threats, and alert officers in real-time, improving situational awareness. This application is especially valuable in crowded or high-risk environments like airports, large events, or public transportation systems.[7]
  4. Enhanced Investigations and Evidence Processing: AI tools also support investigations by processing large amounts of digital evidence quickly and accurately. Investigators often need to sift through massive datasets, such as text messages, social media posts, or financial records, to uncover criminal activities or connections between individuals. AI can assist by identifying relevant patterns, detecting fraudulent activities, or linking people to criminal networks. Additionally, AI-driven forensic tools are increasingly used to analyze digital evidence in cybercrime cases, such as identifying malicious activity on the internet or tracking down cybercriminals.
  5. Reducing Human Error and Bias: AI can help reduce human error and improve the objectivity of policing. Algorithms are designed to make decisions based on data rather than personal judgment, which can sometimes be influenced by unconscious bias. By relying on objective data analysis, AI tools can minimize the risk of biased decision-making in areas like arrest predictions, sentencing recommendations, and parole decisions. For example, AI systems that assess the risk of recidivism can help ensure that decisions about parole are based on a more standardized and evidence-based approach.
  6. Improving Public Trust and Accountability: AI can also play a role in improving public trust in law enforcement by enhancing transparency and accountability. AI systems can be used to track and record police interactions with the public, ensuring that officers' actions are consistent with legal and departmental guidelines. Moreover, AI-enabled body cameras and dash cams can provide valuable evidence in cases of misconduct or disputes, helping to ensure that officers are held accountable for their actions.
 
While the applications and benefits of AI in policing are vast, they must be balanced with careful considerations around privacy, fairness, and the potential for bias in algorithms. As AI technologies continue to evolve, it is crucial for law enforcement agencies to adopt clear regulations and oversight to ensure that these tools are used responsibly and ethically. Nonetheless, when applied thoughtfully, AI has the potential to significantly improve policing practices, enhance public safety, and build more effective and equitable law enforcement systems.
 
 
ETHICAL CONCERNS AND CHALLENGES IN AI-DRIVEN PREDICTIVE POLICING
While AI-driven predictive policing tools offer significant potential for improving law enforcement practices, they also present a range of ethical concerns and challenges that must be addressed to ensure that their use does not harm individuals or communities.[8] These concerns center primarily around issues of bias, privacy, transparency, accountability, and the potential for reinforcing existing inequalities in the criminal justice system.[9]
  1. Bias and Discrimination: One of the most critical ethical concerns surrounding AI in policing is the potential for algorithmic bias. Predictive policing tools often rely on historical crime data, which can reflect systemic biases present in law enforcement practices. For instance, if data shows a higher concentration of policing in minority or low-income neighborhoods, predictive tools might perpetuate these biases by predicting higher crime rates in these same areas. This can lead to over-policing of already marginalized communities, reinforcing harmful stereotypes and exacerbating racial and socio-economic inequalities. The use of biased data in AI systems raises the question of whether these tools can be truly objective or whether they merely replicate and reinforce existing societal biases.
  2. Privacy and Surveillance: AI-powered predictive policing tools often rely on vast amounts of personal and public data, which raises significant privacy concerns. The widespread use of surveillance technologies, such as facial recognition and monitoring of online activity, can infringe on individuals' privacy rights. In some cases, AI tools may be used to track people without their knowledge or consent, leading to concerns about the erosion of civil liberties. Furthermore, the extensive collection and analysis of personal data can create a chilling effect, where people feel they are constantly being watched, which may deter free expression and the exercise of fundamental rights.[10]
  3. Lack of Transparency and Accountability: Many AI systems used in predictive policing operate as “black boxes,” meaning their decision-making processes are not easily understood by the public or even law enforcement officials themselves. This lack of transparency makes it difficult to assess how algorithms reach conclusions or predictions, complicating the ability to hold law enforcement accountable for decisions made using these tools. If an AI system leads to a wrongful arrest, an unjustified surveillance operation, or a biased prediction, it can be difficult to identify who is responsible for the mistake—whether it’s the police, the algorithm, or the data scientists behind the tool. Without clear accountability mechanisms, the potential for harm increases.
  4. Erosion of Human Judgment: The reliance on AI tools in law enforcement can also lead to the erosion of human judgment. While AI can help identify patterns in large datasets, it cannot fully understand the complex social and cultural contexts of criminal behavior. Law enforcement officers might over-rely on AI predictions and make decisions based on algorithmic outputs without fully considering the nuances of individual cases. This could result in inappropriate policing actions or decisions that overlook important contextual information that a human officer might have considered in their decision-making process.
  5. Over-policing and Social Control: AI-driven predictive policing can exacerbate concerns about over-policing, particularly in vulnerable communities. When algorithms predict high crime rates in specific neighborhoods, there may be an increased police presence in those areas, which could lead to heightened scrutiny of residents, increased arrests, and a sense of being constantly surveilled. This kind of "predictive" policing may inadvertently contribute to a cycle of criminalization for certain communities, reinforcing their marginalization and further eroding trust in law enforcement.
  6. Ethical Use and Regulation: Finally, there is the challenge of ensuring that AI tools are used ethically and within a clear regulatory framework. While some jurisdictions have begun to set guidelines for the use of AI in law enforcement, there remains a lack of consistent national or international standards. Without regulation, there is a risk that AI tools could be deployed in ways that prioritize efficiency over fairness, accountability, and justice. Policymakers and law enforcement agencies must work together to establish guidelines that ensure AI-driven tools are used in a responsible manner, with proper oversight and a focus on protecting individual rights.
In conclusion, while AI-driven predictive policing holds great promise for improving the effectiveness of law enforcement, its ethical challenges cannot be ignored. Addressing issues of bias, privacy, accountability, and transparency is essential to ensuring that these technologies are used in a fair, just, and equitable manner. Without careful consideration and regulation, the deployment of AI in policing could perpetuate existing inequalities and undermine public trust in law enforcement.[11]
 
LEGAL AND REGULATORY FRAMEWORKS FOR AI-DRIVEN PREDICTIVE POLICING IN INDIA
As Artificial Intelligence (AI) continues to gain traction in policing globally, India is also exploring the use of AI-driven predictive policing tools to enhance law enforcement practices. However, the legal and regulatory frameworks governing the use of such technologies remain underdeveloped, presenting significant challenges in terms of data privacy, accountability, and ethical considerations. India’s legal landscape for AI in policing is still evolving, and several legal frameworks, along with emerging regulations, need to be considered to address these concerns.[12]
  1. Data Protection and Privacy: India’s approach to data privacy and protection is primarily governed by the Information Technology Act, 2000, particularly under the Reasonable Security Practices and Procedures under Section 43A and Section 72A, which address unauthorized access to personal data. However, the act does not offer comprehensive data protection laws and is often criticized for its inadequacy in addressing the challenges posed by the digital age. The Personal Data Protection Bill, 2019, currently under parliamentary review, aims to establish a more robust data protection framework. This bill seeks to regulate the collection, storage, and processing of personal data by private entities and the government, including law enforcement agencies. If passed, it will impose strict requirements on how police forces use and store data obtained through predictive policing technologies, ensuring that citizens’ privacy rights are respected.
  2. The Right to Privacy: In 2017, the Indian Supreme Court recognized the "right to privacy" as a fundamental right under the Indian Constitution in the K.S. Puttaswamy v. Union of India case. This landmark ruling requires that any use of personal data, including by AI-driven predictive policing tools, must comply with constitutional privacy standards. The use of surveillance technologies, facial recognition systems, and other AI tools in law enforcement must therefore be carefully regulated to avoid violating citizens' privacy rights. The implementation of AI in policing must balance law enforcement needs with the protection of individuals' rights to privacy, ensuring that intrusive measures are justified, proportionate, and transparent.[13]
  3. Regulation of AI and Accountability: Currently, there is no comprehensive national legislation specifically regulating the use of AI in law enforcement. However, various government bodies, such as the Ministry of Home Affairs and National Crime Records Bureau (NCRB), have initiated pilot projects and research into the integration of AI in policing. The National AI Strategy document, which outlines the government’s vision for AI in India, also highlights the need for AI governance frameworks, but it does not yet provide a detailed regulatory framework for policing.
  4. Transparency and Oversight: A significant challenge with AI-driven policing is the lack of transparency in how predictive tools operate. In India, as in other countries, AI algorithms often function as "black boxes," where even the law enforcement agencies using them may not fully understand the decision-making processes. Given the profound implications for civil liberties, it is crucial to establish oversight mechanisms to monitor AI usage in policing. Regular audits, independent reviews, and transparency regarding how data is collected, used, and processed should be mandatory. Law enforcement agencies should also be required to disclose their use of predictive policing tools to the public and ensure that there is accountability in the case of errors or injustices caused by algorithmic decisions.
  5. Ethical and Regulatory Guidelines: India needs comprehensive ethical guidelines to ensure the responsible use of AI in policing. Such guidelines should focus on ensuring that AI tools do not perpetuate biases or discriminatory practices, especially against marginalized communities. Training for law enforcement officers on the ethical implications of using AI technologies is crucial to prevent over-reliance on algorithms and ensure that human judgment remains central in policing decisions.
While India has made strides in regulating digital data and privacy through the Personal Data Protection Bill, there is still much work to be done in creating a specific and comprehensive regulatory framework for the use of AI in policing. The country needs a balanced approach that incorporates both innovation and robust safeguards to protect citizens’ rights. Until clearer laws and regulations are enacted, AI-driven predictive policing in India will remain a complex issue, requiring careful oversight and continuous scrutiny to ensure it is used responsibly.
CASE STUDIES: INDIA
India has been experimenting with the use of Artificial Intelligence (AI) in law enforcement, including the adoption of predictive policing tools and surveillance technologies. While the use of AI in policing is still in its nascent stages in India, there are several case studies where AI tools have been piloted or deployed with varying degrees of success and controversy.
  1. Facial Recognition Technology in Delhi: The Delhi Police has implemented facial recognition technology (FRT) as part of its efforts to improve security and surveillance. In 2019, the Delhi Police launched a pilot project using FRT to identify and track criminals and missing persons. The system is linked to a database of criminal records, allowing officers to scan public spaces and match faces captured through CCTV cameras. In 2020, the police expanded this technology to monitor protests, which raised concerns about mass surveillance and the potential violation of privacy rights. The ethical implications of using FRT, especially in a country with limited privacy laws, have sparked debates about the balance between security and civil liberties.
While FRT has proven useful in identifying criminals and missing persons in some cases, its accuracy has been questioned. Reports suggest that the system often has difficulty accurately identifying people, particularly in crowded or poorly lit environments, and it has been criticized for having a higher error rate for women and people of color. Despite these challenges, the Delhi Police’s adoption of AI-based surveillance reflects the growing trend of using AI tools for law enforcement purposes.
  1. AI-Powered Predictive Policing in Uttar Pradesh: In Uttar Pradesh, one of India’s largest states, the police department experimented with AI-powered predictive policing tools to forecast crime hotspots and improve resource allocation. The Uttar Pradesh Police Department used predictive analytics to identify areas with high crime rates, particularly in the cities of Lucknow and Kanpur. By analyzing historical crime data, such as patterns of theft, assault, and vandalism, AI algorithms provided predictions about where crimes were most likely to occur, allowing police to deploy officers strategically.
While the program demonstrated some success in reducing crime in certain areas, concerns arose regarding the accuracy of the data and the potential for reinforcing biases in law enforcement practices. Critics argued that the reliance on historical crime data could result in over-policing of certain communities, particularly marginalized groups, reinforcing existing inequalities in the justice system. There were also concerns about transparency, as the predictive models used by the police were not made publicly available, preventing independent audits of the algorithm’s fairness and accuracy.[14]
  1. Crime Data Analytics in Maharashtra: In Maharashtra, the police have leveraged AI and machine learning tools to analyze crime data and assist in solving criminal cases. By using advanced analytics to process large volumes of criminal records, including information on modus operandi, locations, and timing of crimes, the system helps law enforcement identify trends and connections between different incidents. This approach has been used in both urban and rural areas to solve complex cases, including organized crime and cybercrimes.[15]
The AI system used in Maharashtra has been credited with improving the efficiency of investigations by enabling officers to access relevant data quickly. However, concerns have been raised about the adequacy of the data used in the system and the risk of algorithmic bias. The system’s effectiveness relies heavily on the quality of the input data, which may reflect historical biases in crime reporting or policing practices. Furthermore, the use of AI in criminal investigations has raised questions about the protection of suspects' rights, especially in terms of how data is collected and used without sufficient oversight.
 
Challenges and Future Considerations: These case studies illustrate the promise and challenges of integrating AI into policing in India. While AI tools offer potential benefits such as improved efficiency, crime prevention, and resource allocation, they also raise concerns about privacy, bias, and accountability. The lack of comprehensive data protection laws and clear regulations for AI use in law enforcement exacerbates these challenges. As AI adoption grows, there is a pressing need for stronger ethical guidelines, transparency in algorithmic decision-making, and safeguards against misuse to ensure that AI in policing serves to enhance, rather than undermine, justice and fairness.
 
CONCLUSION
The use of AI in policing, particularly through predictive policing tools, has the potential to revolutionize law enforcement by enhancing efficiency, improving resource allocation, and proactively preventing crime. However, as demonstrated by case studies in India, the deployment of these technologies brings with it significant ethical, legal, and social challenges. Issues such as algorithmic bias, privacy violations, lack of transparency, and over-policing of marginalized communities are prominent concerns that need to be addressed as AI becomes increasingly integrated into policing practices.
 
While AI can offer solutions to resource constraints and help law enforcement agencies respond faster to emerging threats, it cannot replace human judgment. The ethical implications of using AI, especially regarding fairness and accountability, demand careful consideration and regulation. In India, the absence of a comprehensive legal framework for AI in policing and the slow progress on privacy laws, such as the Personal Data Protection Bill, highlight the need for immediate action to safeguard individual rights and ensure that AI tools are deployed responsibly.
 
Going forward, India must establish clear and robust guidelines and regulatory frameworks to govern AI’s use in policing.[16] This includes ensuring transparency, preventing discrimination, and incorporating ethical safeguards. A balanced approach, which harmonizes the benefits of AI with the protection of civil liberties, will be essential for ensuring that AI in policing serves both the public and law enforcement agencies without infringing upon fundamental rights. Only through thoughtful regulation and oversight can AI technologies contribute positively to a fairer, safer society.

[1] Ethical and Legal Implications of Predictive Policing in India, Indian Journal of Law and Technology (2021).
[2] Kumar, S., & Roy, A. (2022). "The Ethics of AI in Indian Policing: A Critical Perspective." Indian Journal of Technology and Law, 16(3), 213-229.
[3] Artificial Intelligence for Good: Exploring the Impact of AI on Governance, NITI Aayog (2018).
[4] Bhatnagar, S., & Choudhury, N. (2020). "Artificial Intelligence and Its Impact on Law Enforcement in India." Indian Journal of Public Administration, 66(3), 478-496.
[5] Artificial Intelligence in Indian Policing: A Review (2020), International Journal of Law, Crime, and Justice.
[6] Predictive Policing and AI: An Indian Perspective by Raghav Agarwal (2021), India Today.
[7] How AI is revolutionizing Law Enforcement in India by Praveen R (2020), The Economic Times.
[8] AI in Policing: A Blueprint for India's Law Enforcement Agencies, Bureau of Police Research and Development (BPRD), Ministry of Home Affairs (2021).
[9] Nair, P., & Kumari, R. (2021). "AI in Policing: Legal and Ethical Concerns in India." Journal of Law and Technology, 14(1), 22-39.
[10] Sundaram, P., & Sreenivasan, R. (2021). "AI and Surveillance: The Future of Policing in India." Indian Journal of Cyber Law and Security, 12(3), 199-212.
[11] Charlier, J. E. (2018). "AI in Policing: An Emerging Legal and Ethical Landscape." The Georgetown Law Journal, 106(2), 287-313.
[12] Ramasubramanian, V., & Verma, A. (2021). "The Legal Framework for AI in Policing: An Indian Perspective." Indian Journal of Law and Technology, 9(2), 123-138.
[13] Kapur, A., & Soni, M. (2020). "Artificial Intelligence and the Legal Framework for Surveillance in India." Journal of Technology and Law, 24(1), 89-102.
[14] Patel, S., & Dey, R. (2021). "Legal Accountability in the Use of AI in Policing: A Case for Stronger Oversight in India." Indian Law and Technology Review, 14(2), 98-112.
[15] Ibid.
[16] Das, T., & Singh, S. (2022). "Artificial Intelligence and the Need for Legal Reforms in Policing: The Indian Perspective." Indian Journal of Public Policy and Law, 7(1), 57-70.

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ARTIFICIAL INTELLIGENCE DRIVEN PREDICTIVE POLICING TOOLS: RESHAPING LAW ENFORCEMENT PRACTICES BY - RITUL ARYAN

Authors: RITUL ARYAN
Registration ID: 103616 | Published Paper ID: WBL3616
Year: Jan - 2025 | Volume: 3 | Issue: 1
Approved ISSN: 2581-8503 | Country: Delhi, India
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