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1. Fairness in Algorithmic Policing

   https://doi.org/10.1017/apa.2021.39  

   Purves, Duncan (March 2022, Journal of the American Philosophical Association)

   Predictive policing, the practice of using of algorithmic systems to forecast crime, is heralded by police departments as the new frontier of crime analysis. At the same time, it is opposed by civil rights groups, academics, and media outlets for being ‘biased’ and therefore discriminatory against communities of color. This paper argues that the prevailing focus on racial bias has overshadowed two normative factors that are essential to a full assessment of the moral permissibility of predictive policing: fairness in the social distribution of the benefits and burdens of policing as well as the distinctive role of consent in determining fair distribution. When these normative factors are given their due attention, several requirements emerge for the fair implementation of predictive policing. Among these requirements are that police departments inform and solicit buy-in from affected communities about strategic decision-making and that departments favor non-enforcement-oriented interventions. 

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2. The Indianapolis harmspot policing experiment

   https://doi.org/10.1016/j.jcrimjus.2021.101814  

   Carter, Jeremy G.; Mohler, George; Raje, Rajeev; Chowdhury, Nahida; Pandey, Saurabh (May 2021, Journal of Criminal Justice)

   null (Ed.)
3. Predictive Policing: A Mathematical Primer

   https://doi.org/10.1090/noti2968  

   Johnson, Joseph; McKenzie, Theo; Wong, Tian An (August 2024, Notices of the American Mathematical Society)
4. Proactive policing as reinforcement learning

   Kinsman, Dawson; Wong, Tian_An (May 2023, dblp)
5. Runaway Feedback Loops in Predictive Policing

   Ensign, Danielle; Friedler, Sorelle A; Nevlle, Scott; Scheidegger, Carlos; Venkatasubramanian, Suresh (February 2018, Proceedings of the 1st Conference on Fairness, Accountability and Transparency)

   Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been shown susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which reported incidents of crime (those reported by residents) and discovered incidents of crime (i.e those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest. 

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