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1. Changes in public–police cooperation following the murder of George Floyd

   https://doi.org/10.1093/pnasnexus/pgac189  

   Brantingham, P. Jeffrey; Mohler, George; MacDonald, John; Pescosolido, ed., Bernice (September 2022, PNAS Nexus)

   Abstract The murder of George Floyd triggered a broad social response and noticeable shift in public opinion of policing. Since policing is dependent upon public cooperation, a question is whether the shift in opinion had an effect on police calls-for-service. Data from Los Angeles and New York City are evaluated using a regression discontinuity design. Violent crime, property crime, and quality-of-life disorder calls are analyzed to address differences in reporting norms. The role of racial–ethnic composition of local areas is assessed by examining census tracts where the majority of the residential population is Asian, Black, Hispanic, or White. Following the murder of George Floyd, in New York City violent crime, property crime, and quality-of-life calls all increased significantly. In Los Angeles, quality-of-life calls for service decreased significantly. The increase in violent crime calls observed in New York was greatest in areas where the majority of residents are Black, whereas the increase in property crime calls was the greatest in areas where a majority of residents are White. The decrease in quality-of-life calls observed in Los Angeles was in areas where the majority of residents are White. In both cases, the effect was relatively short-lived. 

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2. 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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3. Runaway Feedback Loops in Predictive Policing

   Ensign, Danielle; Friedler, Sorelle A.; Neville, Scott; Scheidegger, Carlos; Venkatasubramanian, Suresh (January 2018, Conference of 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 empirically shown to be 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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4. Exploring the Impact of (Not) Changing Default Settings in Algorithmic Crime Mapping - A Case Study of Milwaukee, Wisconsin

   https://doi.org/10.1145/3311957.3359500  

   Haque, MD Romael; Weathington, Katherine; Guha, Shion (November 2019, CSCW ’19 Companion)

   Policing decisions, allocations and outcomes are determined by mapping historical crime data geo- spatially using popular algorithms. In this extended abstract, we present early results from a mixed- methods study of the practices, policies, and perceptions of algorithmic crime mapping in the city of Milwaukee, Wisconsin. We investigate this differential by visualizing potential demographic biases from publicly available crime data over 12 years (2005-2016) and conducting semi-structured interviews of 19 city stakeholders and provide future research directions from this study. 

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5. Fraud in disaster: Rethinking the phases

   https://doi.org/https://doi.org/10.1016/j.ijdrr.2019.101232  

   B.E. Aguirre, David Lane (July 2019, International journal of disaster risk reduction)

   This paper reexamines the link between disasters and crime to argue that the relationship between the two becomes more evident once the diachronic nature of the disaster is specified. The first section appraises the literature on crime and disaster and calls for more attention to this topic. The subsequent sections of the paper present multiple evidence for the occurrence of crimes and fraud in disasters and the results of preliminary empirical research using U.S. Department of Justice information on the reported white collar and other types of crimes occurring in the aftermath of the Katrina Hurricane and the British Petroleum Gulf Coast oil spill. The findings indicate that crimes and frauds occur during the earliest phases of disasters and that the probability for the occurrence of costlier crimes, akin to white-collar crime, increases, as time elapses. The conclusion calls for a reconceptualization of the phases of disasters. 

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