Predictive Policing Research Articles

Balancing Safety and Justice, the Ethics of Predictive Policing

Examining the ethical ramifications of predictive policing in South Africa, the research aims to pinpoint major obstacles and dangers while examining potential solutions to allay worries and guarantee responsible application. Key subjects include data privacy, algorithmic bias, over policing, community involvement, accountability and transparency, and transparency. reduction of bias, oversight procedures, Important Points, Significant ethical issues are brought up by predictive policing in South Africa, including invasions of privacy, algorithmic prejudice, and over policing. Given the historical and current backdrop of racial inequality, these worries are more pressing. In order to address these issues, a balanced strategy that puts individual rights and public safety first is needed. In order to guarantee that predictive policing is used responsibly in South Africa, transparency, accountability, community involvement, and ethical standards are crucial. It is essential to mitigate bias in algorithms and data in order to stop biased results. An over-reliance on predictive policing can be detrimental to community relations and conventional enforcement techniques. Methodological strategy: Designing qualitative research, review of the literature, semi-structured interviews with members of the public, law enforcement officers, and predictive policing specialists Case studies of South African predictive policing initiatives, Content study of South African media reports on predictive policing. Relevance. The work advances our knowledge of the moral conundrums raised by predictive policing in South Africa in a more comprehensive and nuanced way. The study can serve to raise public awareness and encourage participation in the discussion over predictive policing in South Africa. The findings can assist policymakers and law enforcement agencies in creating moral and practical strategies for implementing predictive policing. In conclusion, there are advantages and disadvantages to predictive policing in South Africa. Although technology presents the possibility of more effective and efficient law enforcement, it also brings up serious ethical issues. South Africa can minimize the dangers associated with predictive policing while maximizing its potential advantages by addressing these issues and putting the necessary procedures in place.

ARTIFICIAL INTELLIGENCE IN CRIMINAL JUSTICE MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW

This systematic review, based on 37 articles, explores the role of artificial intelligence (AI) in criminal justice, focusing on its applications in predictive policing, judicial risk assessments, and surveillance, as well as the associated ethical and regulatory challenges. AI has demonstrated substantial potential for improving efficiency and accuracy in criminal justice systems, from optimizing law enforcement resource allocation to providing data-driven risk assessments that support judicial decisions. However, the review identifies significant ethical issues, especially related to algorithmic bias, which can perpetuate existing societal inequalities and disproportionately affect marginalized communities. Concerns around transparency and accountability are prevalent, as the "black-box" nature of many AI algorithms complicates public understanding and trust in AI-driven outcomes. Surveillance tools, including facial recognition and behavioral analysis, enhance real-time threat detection but raise privacy and civil rights concerns, highlighting the need for regulatory oversight. Gaps in legal frameworks suggest the urgency for standardized policies that address data privacy, algorithmic fairness, and accountability in AI applications. The findings underscore that interdisciplinary collaboration, transparent practices, and comprehensive regulatory measures are essential to responsibly integrate AI into criminal justice, balancing technological advancements with justice, equity, and public trust.

Insights into Algorithmic Decision-Making Systems via a Decolonial-Intersectional Lens: A Cross-Analysis Case Study

As the line between governance, technology, and decision-making becomes ever-blurred, advocates and disparagers of Artificial Intelligence (AI) debate the impacts of its societal integration. Despite efforts to promote Fairness, Accountability, Transparency, and Ethics (FATE), vulnerable populations continue to be systematically marginalized and made “invisible” by the racialised, classed, and colonial institutions buttressing Algorithmic Decision-Making Systems (ADMS). To address these sociotechnical risks and acknowledge our privileged, Western “standpoint epistemology,” we employ a “metaparadigm perspective” to engage the literature and case studies through a critical theory lens. The cross-analysis of three case studies: Systems Theory, the United States’ “Blueprint for an AI Bill of Rights,” and predictive policing demonstrate that our current risk mitigation frameworks are incapable of addressing transhistorical injustices. Therefore, we call for operationalizing intersectional risk theory via the decolonial turn to address ADMS risks. On that note, we propose three algorithmic accountability recommendations. We theorize that through a reformulation of FATE principles to that of the algorithmic (decolonial) self, understanding power imbalances through marginalized risk discourses, and advancing intersectional ML decision-making practices, policymakers may be better positioned to enact equitable and just ADMS.

Evaluating the Effectiveness of Predictive Policing in Nigeria: A Qualitative study of law Enforcement Practices in Lagos State

This qualitative study evaluates the effectiveness of predictive policing technologies by examining the implementation within law enforcement operations in Lagos State. The research aims to explore how predictive systems influence crime prevention, and decision-making processes in policing. Drawing on semi-structured interviews with NDLEA officers, the study reveals the perceived benefits and challenges of integrating predictive policing tools into traditional methods. Through thematic analysis of interviews and review of secondary sources, the research identifies key themes such as the tension between technology and human judgement, and the potential for over-reliance on system predictions. The findings indicate that while predictive policing can improve efficiency in crime hotspot identification, its efficacy is undermined by concerns over privacy, and fairness. The study concludes that for predictive policing to be effective, it must come with strong regulatory frameworks and ethical guidelines to ensure support from technologies. This research contributes to the growing discourse on the intersection of technology and law enforcement, offering insights into how predictive policing can evolve to better serve the people.

PREDICTIVE POLICING BY BHABINKAMTIBMAS AS A SOLUTION TO ADDRESS CRIMINALITY IN CILEMPUNG VILLAGE, PASIRJAYA, KARAWANG

The Karawang Regency faces a significant increase in the number of crimes, being a major concern due to its disruptive impact on the community as well as serious challenges for law enforcement and local governments. In order to respond to the various problems faced by Bhabinkamtibmas in the field, support and strengthening are needed with the right policing program. The research contributes to the field of public security by introducing a Predictive Policing framework tailored to the needs of Cilempung Village, Karawang, utilizing information technology and data analysis to identify crime patterns and predict potential crimes. The study provides insights into the factors influencing crime rates and offers practical strategies for implementing predictive polcing in local communities, ultimately aiming to enhance public safety and maintain social order.

Predictive Policing: The Role of AI in Crime Prevention

Predictive Policing: The Role of AI in Crime Prevention

Front and Back Covers, Volume 40, Number 5. October 2024

Front and back cover caption, volume 40 issue 5ALGORITHMIC POLICING & AIThis image captures Roberto J. González's exploration of algorithmic policing in America in this issue. Cameras survey a stylized urban landscape, symbolizing the pervasive reach of artificial intelligence in modern law enforcement.González examines various technologies of surveillance: predictive policing platforms, facial recognition software and acoustic gunshot detection systems. Its algorithmic composition hints at the complex mathematical models underpinning these tools, which claim to forecast crime and identify potential offenders.Below, the cityscape appears as a mix of physical structures and data points, blurring the line between the tangible world and its digital representation. This visual metaphor represents the technologies that are reshaping our understanding of urban spaces, often reinforcing existing patterns of surveillance and control in marginalized communities.The ominous tone and starkness of the image echo the article's critical perspective on the unchecked expansion of algorithmic policing. The article raises questions about privacy, consent, and the potential for these systems to amplify biases and errors in law enforcement decisions.The human stories behind these technologies – from wrongful arrests to community distrust – are often obscured by promises of efficiency and safety. This reminds us of the need for anthropological scrutiny of these roboprocesses and their impact on social fabrics.The unseen digital infrastructure is increasingly shaping policing practices, reshaping the landscape of public safety and surveillance in America and beyond. This raises important questions about how venture capital, tech startups, and law enforcement agencies are transforming traditional notions of policing and civil liberties in the digital age.Back cover caption, volume 40 issue 5HANDS OF CHANGE: FARMER IDENTITYThis image of the weathered hands of a farmer, cradling rich soil, encapsulates the complex reality of modern European agriculture explored in Eimear Mc Loughlin's article. These hands, which have long symbolized the deep connection between farmers and the land they cultivate, now tell a more nuanced story of an identity in flux.According to Mc Loughlin, the traditional image of the farmer is vanishing because of new pressures that are becoming more intense. These hands, once primarily engaged in the tactile work of cultivating crops and tending to animals bred for food, now increasingly grapple with spreadsheets, regulatory paperwork and the invisible constraints of global markets. The soil they hold represents not just the promise of growth, but also the weight of environmental concerns, stringent regulations and societal expectations.Yet, this image also speaks to the enduring essence of farming that persists despite these challenges. It reminds us of the irreplaceable role farmers play in our food systems and national identities, even as their traditional image evolves. The rich soil suggests the potential for renewal and adaptation, mirroring Mc Loughlin's call for a reimagined agricultural future.As Europe's farmers protest and struggle with their changing identity, this image reminds us of what is at stake. It challenges us to consider how we can support a transition to sustainable farming that reflects the hands‐on work symbolized here while addressing pressing environmental and economic realities. In doing so, we might forge a path where the figure of the farmer does not disappear but evolves, maintaining that crucial connection to the land while navigating the complexities of modern agriculture.

Algorithmic policing: Part 1. Tech startups, venture capital and law enforcement in America

This article examines the rise of algorithmic policing in the United States, focusing on predictive policing platforms and facial recognition technologies. Part 1 explores how companies like PredPol, Palantir and Clearview AI have developed data‐driven tools to predict and prevent crime and identify suspects. The article analyses the historical context, technological development and implementation of these systems by law enforcement agencies. It also discusses the concerns raised by critics, including issues of privacy, racial bias and the potential for reinforcing discriminatory policing practices. Through case studies and examples, the article illustrates how these technologies have impacted communities, particularly communities of colour. It examines the complex interplay between Silicon Valley startups, venture capital and law enforcement in shaping modern policing practices.

The ‘Predelinquent’ and the Community: Psychiatric Surveillance and Predictive Policing in Interwar Berkeley

Summary Through the 1920s and 1930s, the Berkeley Police Department, renowned as a centre of scientific training and investigation, developed new programmes of predictive policing targeting ‘predelinquent’ youth. Led by Chief August Vollmer, schools, charities, social services and families throughout Berkeley were coordinated in the ongoing detection of early signs of developing psychoses and personality disorders believed to lead to future criminality. Implying a malleable trajectory of habit formation which might be perverted or corrected, predelinquency warranted psychiatric surveillance across the community to assist Berkeley’s police in identifying, mapping and correcting at-risk children. This paper examines how, through the psychiatric category of predelinquency, law enforcement enrolled the community in networks of pre-emptive surveillance with new responsibilities for reporting and correction. In turn, I examine how predelinquency shifted to accommodate various local priorities and anxieties, whereby predictive policing’s conceptions of potential threat or improvability reproduced the boundaries of the normative American community.

뉴욕경찰청의 알고리즘 예측치안 전략의 논쟁점

The purpose of this study is to examine the controversies surrounding the algorithmic predictive policing strategy adopted by the New York Police Department. NYPD is very active in pursuing algorithmic predictive policing strategies, but it is facing a variety of criticisms, including: First, personal information collection activities for predictive security violate the ideology of the Fourth Amendment to the U.S. Constitution, which prohibits invasion of privacy. Second, as the police rely on instructions generated by algorithmic predictions, police operations are becoming dehumanized. Third, the police exercise their police authority intensively against dangerous people or dangerous areas, and there is a risk that these people will eventually be negatively stigmatized and become criminals. Fourth, the police unilaterally exercise police power based on algorithmic predictions, rather than a two-way police service that reflects the needs of citizens or the community. Fifth, the efficiency of algorithmic predictive policing has not been verified, and the problem of contamination of used big data is being raised. Sixth, it is impossible to confirm whether the data provided by the police to private companies is opaque and secure. Seventh, there is the irrationality of Police power being replaced by private companies' for-profit businesses. Eighth, the New York Police Department is subject to monitoring of algorithmic predictive policing through the Local Law in Relation to Automated Decision Systems Used by Agencies and Public Oversight of Surveillance Technology(POST) Act, but the problems remain unresolved. Ninth, the New York Police Department continues to engage in legal disputes with human rights watchdog groups such as the Brennen Center over the non-disclosure of algorithmic predictive security information, losing most cases, raising questions about the legality of big data collection and management.

THE ETHICS OF AI AND AUTOMATED DECISION-MAKING SYSTEMS

As artificial intelligence (AI) and automated decision-making systems become more advanced and prevalent, they raise significant ethical concerns that need to be carefully considered. These technologies have the potential to revolutionize various fields, from healthcare to finance, but they also pose risks if not developed and deployed responsibly. From predictive policing and risk assessment tools in the criminal justice system, to automated resume screening and hiring at companies, to medical diagnostic AI - automated decision systems are shaping profound aspects of our society. Keywords: Ethics of AI,Automated Decision Making System

The Practice of Predictive Identification: Optimising for Organisational Needs

The advent of predictive policing systems demonstrates an increased interest in more novel forms of data processing for the purpose of crime control. This paper draws on interviews with police practitioners in the Netherlands and the UK to deconstruct the rationalities that are embedded within the turn to predictive identification. Debates on predictive policing have predominantly centred data in the analysis of the institutional and societal implication of prediction, linking its use to the premise of efficiency and accuracy and foregrounding issues around bias and discrimination. Yet, little is known about its actual practice. In policing, I find that studying data as practice surfaces new insights into the relationship between risk and the ways in which crime priorities are operationalised and the security mandate of the state is negotiated. Drawing on Harcourt’s (2008) observation that the desire to predict crime says more about the police than it does about a potential offender, I argue that predictive identification is not about prediction, nor about efficiency, but rather it is about optimisation. Here, datafication serves to overcome self-defined organisational challenges within the police.

Algorithmic fairness in predictive policing

AbstractThe increasing use of algorithms in predictive policing has raised concerns regarding the potential amplification of societal biases. This study adopts a two-phase approach, encompassing a systematic review and the mitigation of age-related biases in predictive policing. Our systematic review identifies a variety of fairness strategies in existing literature, such as domain knowledge, likelihood function penalties, counterfactual reasoning, and demographic segmentation, with a primary focus on racial biases. However, this review also highlights significant gaps in addressing biases related to other protected attributes, including age, gender, and socio-economic status. Additionally, it is observed that police actions are a major contributor to model discrimination in predictive policing. To address these gaps, our empirical study focuses on mitigating age-related biases within the Chicago Police Department's Strategic Subject List (SSL) dataset used in predicting the risk of being involved in a shooting incident, either as a victim or an offender. We introduce Conditional Score Recalibration (CSR), a novel bias mitigation technique, alongside the established Class Balancing method. CSR involves reassessing and adjusting risk scores for individuals initially assigned moderately high-risk scores, categorizing them as low risk if they meet three criteria: no prior arrests for violent offenses, no previous arrests for narcotic offenses, and no involvement in shooting incidents. Our fairness assessment, utilizing metrics like Equality of Opportunity Difference, Average Odds Difference, and Demographic Parity, demonstrates that this approach significantly improves model fairness without sacrificing accuracy.

The weather proxy: the racial technics of algorithmic surveillance

ABSTRACT This article examines the political work performed by the weather within contemporary big data predictive analytics. As a sociotechnical construction, the weather has long served as a predictive medium for forecasting human behaviour across historical contexts in order to reinforce settler colonial and imperial projects of spatial domination. Now, within twenty-first century big data systems, the weather has become a proxy for predicting human activity. Examining industrial materials from the predictive policing software HunchLab and the social media sentiment analysis platform Social Doppler, I argue that the weather proxy underwrites claims to prediction by collapsing the distinction between correlation and causation. Key here is how the proxy remediates the mechanism of impressibility that structured the weather as a racial technique for population management within nineteenth century statistical research. Through the weather proxy, big data programmes channel biopolitical frameworks of impressibility – or impressibility by proxy – in ways that causally bind individual behaviour to group-differentiated vulnerabilities of environmental exposure. At stake is how the weather proxy serves as a universal baseline condition for predicting population-level behaviour, which aims to mitigate accusations of racial bias by deferring to environmental conditions beyond human design. As a cultural construction tied to ubiquity, the weather thus is a rhetorical device that works to legitimate predictive analytics as impartial and universal while simultaneously effacing the violence that inheres within algorithmic technologies. Ultimately, by taking a comparative historical approach to the weather, I argue that we might better grasp the transformations in data science that underpin how ubiquity operates as a site of racial violence within contemporary surveillance systems.

The Role of Artificial Intelligence in Criminal Justice

This study explores the transformative role of artificial intelligence (AI) in the criminal justice system, examining its applications, benefits, and potential challenges. AI technologies, including machine learning, predictive analytics, and natural language processing, are increasingly integrated into various facets of criminal justice, from law enforcement and legal processes to corrections and rehabilitation. Through a comprehensive literature review, this research analyzes how AI enhances the efficiency and effectiveness of criminal justice operations. Key findings reveal that AI can significantly improve crime prediction and prevention, aid in evidence analysis, streamline administrative tasks, and support decision-making processes. For instance, predictive policing models using AI can identify crime hotspots and allocate resources more effectively, while AI-driven tools can assist in analyzing large volumes of legal documents and evidence. However, the study also highlights critical concerns related to bias, fairness, transparency, and ethical implications. There is a growing need for frameworks that ensure AI applications in criminal justice are transparent, accountable, and aligned with ethical standards to prevent discrimination and protect civil liberties. The role of AI in criminal justice presents a dual-edged sword, offering significant advancements while posing substantial risks if not properly managed. This study provides a balanced perspective, offering insights for policymakers, practitioners, and researchers on leveraging AI for a more just and efficient criminal justice system. Future research directions are proposed to address the ethical challenges and to develop robust regulatory frameworks for AI in criminal justice.

Precog visions: Predicting the future with the Minority Report sociotechnical imaginary.

The 2002 film Minority Report regularly appears in tech press articles asking whether it 'predicted the future'. When such publications invoke the film as having 'predicted the future' or 'come true', what social and political claims are being made? How has Minority Report become a discursive tool for imagining, constructing, and criticizing sociotechnical worlds? In this paper, we evaluate the worldbuilding process and real-world trajectories of three technologies 'from' Minority Report, as refracted through the lens of tech journalism: gestural interfaces, targeted advertising, and predictive policing. We argue that science fiction does more than represent technologies; it participates in their social construction. Some technologies imagined in Minority Report operate as 'diegetic prototypes', and the journalistic witnessing public takes them up in complex ways, interpreting, misinterpreting, and remixing the technologies depicted in the film. We further argue that it is not only technologies that move between film and reality in this process, but entire sociotechnical imaginaries. We find that in tech beat interpretations of Minority Report, the interfaces between bodies and technologies reflect a Silicon Valley sociotechnical imaginary of disembodied cyborg subjects and deracialized surveillance that materially and discursively shapes how technologies depicted in the film are developed and received.

Criminology and Social Impact in The Age of Artificial Intelligence [AI

The use of technology has permeated all facets of life, and brought about both positive and negative effects. Criminology as a fields has not been left behind and criminologists are developing various technological tools, including Artificial Intelligence (AI) models to use in detecting, managing and preventing criminal activities. This is a significant step considering that criminals have also found it convenient to use technology as a tool for perpetuating their activities. This paper focused on the adoption of AI in criminology, exploring the attendant benefits of its adoption; the negative social impact of its use and interventions that should be put in place to curb the negative ramification. Some of the beneficial use discussed in the paper include predictive policing, and crime risk assessment, which aids in preventing occurrence of criminal activities. However, the use of these AI models, while beneficial to these criminological functions have presented significant social implications, which include bias and discrimination that perpetuate social stereotypes; privacy breach that lead to the victimization of innocent people; opaque decision making that lead to distrust in the output by the AI tool; and unfair distribution of employment opportunities. The paper concluded that the adoption of AI in criminology is inevitable considering the digital era in which we are currently living in. However, while the benefit of the use of these technologies are varied and welcome, there is a need for ensuring that the legal, social and ethical concerns are adequately addressed. The paper, therefore, recommended the establishment of robust regulatory framework that guide the use of the AI models by law enforcement agencies; the integration of the use of the AI tools with human oversight; the inclusion and transparency and accountability in the operationalization of the tools; collaboration amongst the stakeholders. This paper has used recent extant literature to examine the intersection between criminology and social impact with respect to the use of Artificial Intelligence (AI)

POLICIAMENTO PREDITIVO E INTELIGÊNCIA ARTIFICIAL: ANÁLISE DE DESEMPENHO DO ALGORITMO DE APRENDIZADO DE MÁQUINA SUPERVISIONADO RANDOM FOREST NA PREDIÇÃO DE OCORRÊNCIAS POLICIAIS DE ROUBO NAS ZONAS DA REGIÃO METROPOLITANA DE SÃO LUÍS

Predictive policing is a concept based on the premise that it is possible to predict when and where crimes will occur again in the future by using sophisticated computer analysis based on data from previously committed crimes (UCHIDA, as cited in NORTON, 2013, pp. 32–33). The aim of this article is to analyze the spatial prediction of crimes, highlighting the importance of innovative approaches in the field of public safety management. Specifically, the article explores practical applications of using Artificial Intelligence (AI) through supervised machine learning algorithms for classification, using the emergency call records database (190) of robbery incidents in the metropolitan region of São Luís as input, and comparing results through validation techniques for the Random Forest algorithm. Through detailed analysis of these data, the potential of predictive policing is illustrated not only to anticipate criminal events but also to develop a computational tool capable of assisting and supporting strategic decision-making, significantly contributing to troop allocation management and, consequently, to the enhancement of preventive urban patrol services.

The relevance of the data collection process in the VioGén system from a feminist perspective

The VioGén system is a predictive policing and algorithm tool, which has been implemented in Spain since 2007. Its main objective is to assess the risk of a woman of being victim of intimate partner violence and proposing measures for her protection accordingly. This work will focus on one of the most essential parts of the tool’s functioning: the data collection process. I intend to illustrate how this process of data collection can be influenced by the bias and stereotypes that have been constructed within criminal law concerning gender violence victims and the way this affects, in the end, the system’s functioning.

Ethical Implications of AI in Criminal Justice: Balancing Efficiency and Due Process

The integration of Artificial Intelligence (AI) into criminal justice systems presents a complex landscape of opportunities and challenges. This research article critically examines the ethical implications of AI applications in various facets of criminal justice, from predictive policing to sentencing algorithms. While AI promises enhanced efficiency and data-driven decision-making, it simultaneously raises significant concerns about fairness, transparency, and the preservation of due process rights. This study employs a multidisciplinary approach, combining legal analysis, ethical philosophy, and empirical research to evaluate the current state of AI in criminal justice and project future trajectories. We analyze case studies from multiple jurisdictions, highlighting both successful implementations and controversial failures of AI systems in law enforcement and judicial processes. Our findings reveal a tension between the pursuit of efficiency through AI and the fundamental principles of due process. We identify several key ethical challenges, including algorithmic bias, lack of transparency in decision-making processes, and the potential erosion of human judgment in critical legal determinations. The research also uncovers disparate impacts on marginalized communities, raising concerns about the exacerbation of existing inequalities within the justice system. To address these challenges, we propose a comprehensive ethical framework for the development and deployment of AI in criminal justice. This framework emphasizes the need for continuous human oversight, regular audits of AI systems, and the establishment of clear accountability mechanisms. We argue for a balanced approach that leverages the benefits of AI while safeguarding individual rights and maintaining the integrity of judicial processes. The article concludes by outlining policy recommendations and best practices for lawmakers, judiciary, and law enforcement agencies. These recommendations aim to foster responsible innovation in criminal justice AI, ensuring that technological advancements align with ethical standards and constitutional protections. Our research contributes to the ongoing dialogue on AI ethics in the legal domain and provides a roadmap for the ethical integration of AI in criminal justice systems worldwide.