A review of predictive policing from the perspective of fairness

Alikhademi, Kiana; Drobina, Emma; Prioleau, Diandra; Richardson, Brianna; Purves, Duncan; Gilbert, Juan E.

doi:10.1007/s10506-021-09286-4

Cited by 48 publications

(36 citation statements)

References 31 publications

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“…For one thing, these abstract principles require a tremendous amount of work to operationalize, and they have led to disagreements at the technical level around what measures of success might be appropriate for judging, for example, the fairness of a model (Alikhademi et al 2021) or its accountability (Wieringa 2020). Canca (2020), for example, belabors the point that operationalizing AI principles will differ across domains, and the meaning of specific values will change across contexts, mentioning specifically how the value of transparency might differ across law enforcement, sustainability, and medical applications (20-21).…”

Section: Motivationmentioning

confidence: 99%

Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning

et al. 2022

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In this paper, we outline a new method for evaluating the human impact of machine-learning (ML) applications. In partnership with Underwriters Laboratories Inc., we have developed a framework to evaluate the impacts of a particular use of machine learning that is based on the goals and values of the domain in which that application is deployed. By examining the use of artificial intelligence (AI) in particular domains, such as journalism, criminal justice, or law, we can develop more nuanced and practically relevant understandings of key ethical guidelines for artificial intelligence. By decoupling the extraction of the facts of the matter from the evaluation of the impact of the resulting systems, we create a framework for the process of assessing impact that has two distinctly different phases.

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Section: Motivationmentioning

confidence: 99%

Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning

et al. 2022

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“…In the field of criminal justice, big data technology is valued by police departments in various countries, and big data technology is widely used in the daily judicial activities of police officers. The Los Angeles Police Department (Egbert, 2018) in the US, the West Midlands Police Department (Pramanik, Lau, Yue, Ye, & Li, 2017) in the UK, the Munich Police Department (Egbert & Krasmann, 2020) in Germany, the Basel Police Department (Egbert & Krasmann, 2020) in Switzerland, the Netherlands Amsterdam Police Department (Peeters & Schuilenburg, 2018), Delhi Police Department in India (Alikhademi et al, 2022) and the South African Police Service (Pramanik et al, 2017) all use big data technologies in their daily judicial activities to prevent and control crime (Wang, 2019). Big data technology is officially recognised to some extent in the police departments of various countries.…”

Section: A Broad Overview Of Big Data Developmentmentioning

confidence: 99%

Risk and Governance Strategies: China's Big Data Policing in the Context of Marxian Alienation Theory

Zhang

2022

IJSSS

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Big data technology has been deeply integrated into all areas of society and has had a profound impact on all areas. Big data technology has also brought a series of impacts, some even disruptive to the justice sector. In judicial activities, police departments in various countries are using big data technology extensively in two functions: crime prevention and crime fighting. In crime prevention, big data technology is mainly used to predict where crimes are likely to be committed and the subjects or victims of the crimes that are likely to be committed. In crime fighting, big data technology can help the police quickly identify or analyse a range of information relating to a suspect, including socioeconomic, interpersonal and object relationships. However, some risks of alienation may also arise in the process of police crime prevention and crime fighting. This paper attempts to apply the unique perspective of Marx's alienation theory, focusing on the fascinating Eastern country, to firstly analyse in depth a series of alienation risks that may arise from the use of big data technology analysis in Chinese policing activities, and then, on this basis, propose a few targeted governance strategies accordingly, to provide useful experiences for scholars studying big data policing activities and those concerned with big data applications in Chinese policing activities.

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“…From a technological standpoint, this makes sense: crime is neither a random nor a deterministic process. Some features exist to characterise it [23][24][25]. Given that machines are better than humans at processing multiple inputs simultaneously, computers running appropriate algorithms should be able to assist police officers and leadership.…”

Section: Algorithmic Policing Tools An Overviewmentioning

confidence: 99%

Efficiency of Algorithmic Policing Tools: a nod to C.N. Parkinson

McKay¹,

Richard²

2022

Proceedings of the Canadian Conference on Artificial Intelligence

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Jurisdictions around the world have adopted algorithmic policing technologies. The response to concerns about fairness and privacy often includes a claim that these tools are necessary to control crime without ballooning the size of police departments. Thus, effectiveness and payroll are put forward as factors to balance against fairness concerns. However, there is little evidence to suggest that the deployment of such tools without significant new hiring can improve policing outcomes. AI tools increase the volume and flow rate of police information. In order for these tools to be effective, police must be able to keep up with that flow, while also ensuring the integrity of the algorithm and incoming data over time. We argue that algorithmic policing tools require the hiring of additional resources. These new resource demands represent a digital variant of Parkinson's Law. Given the documented risks these tools pose to some populations, it is necessary to work towards a mathematical understanding of the minimum incremental staffing needed to enable such tools to operate successfully, potentially providing benefits greater than their harms. In an effort to stimulate further research in this area, we offer a preliminary "digital Parkinson's law" reflecting a first attempt to quantify the mathematical relationships between the factors involved.

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A review of predictive policing from the perspective of fairness

Cited by 48 publications

References 31 publications

Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning

Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning

Risk and Governance Strategies: China's Big Data Policing in the Context of Marxian Alienation Theory

Efficiency of Algorithmic Policing Tools: a nod to C.N. Parkinson

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