{"id":33214,"date":"2018-11-13T16:40:20","date_gmt":"2018-11-13T21:40:20","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-at-predpol-risks-biases-and-opportunities-for-predictive-policing\/"},"modified":"2018-11-13T16:40:20","modified_gmt":"2018-11-13T21:40:20","slug":"machine-learning-at-predpol-risks-biases-and-opportunities-for-predictive-policing","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-at-predpol-risks-biases-and-opportunities-for-predictive-policing\/","title":{"rendered":"Machine Learning at PredPol: Risks, Biases, and Opportunities for Predictive Policing"},"content":{"rendered":"

Machine learning and artificial intelligence (AI) have the potential to revolutionize systems and power structures that have existed in society for generations. However, the performance of AI-enabled tools can either be limited or enhanced by the quality and quantity of the data on which their algorithms are based. This paper examines the role of machine learning in creating law enforcement tools to analyze, track, and ultimately attempt to predict<\/em> crime. Predictive policing refers to the use of analytical techniques by law enforcement to make statistical predictions about potential criminal activity [1]. A 2011 report published by researchers at 性视界 Kennedy School and the National Institute of Justice described the advancement of science (defined in this case as both technology and social science) in policing as essential to the retention of public support and legitimacy [2]. The success of these new tools is contingent on their ability to reconcile machine learning solutions for the future with a long history of biased practices and data from the past. PredPol is a private technology company based in Santa Cruz, California that aims to predict crime using cloud software technology that identifies the highest risk times and places in near real-time [3].<\/p>\n

PredPol\u2019s value proposition to law enforcement agencies is three-fold. First, the company offers predictive policing through its machine learning algorithm based on three data points: crime type, crime location, and crime date\/time. The algorithm functions by predicting crime in 500 square foot boxes with the number of boxes deployed in each shift calibrated to the policing resources available. Second, their platform provides mission planning and location management services that use the algorithm results to determine high probability crime locations and track \u201cpatrol dosages\u201d or the amount of time officers spend in PredPol boxes. Finally, PredPol offers an analytics and reporting module that allows for custom reports by any combination of crime types, missions, districts, etc., over a defined date range (See Exhibit 1 for a visual representation of services from PredPol) [4]. PredPol is currently being used by more than 50 police departments across the United States and a few forces in the UK [5]. The company appears energized by the success of their product in law enforcement and is actively entering new markets. This year, PredPol announced a new product offering for private sector and corporate customers with an emphasis on security, patrol, and prevention. They also announced a partnership with one of the largest sheriff departments in the country to tackle the Opioid epidemic. PredPol intends to leverage data from their machine learning algorithm around specific crime patterns to identify areas that signal higher risks of overdoses. Their goal is to share this data with partner entities (faith-based organizations, community groups, etc.) to take a proactive approach to solving the Opioid crisis [6].<\/p>\n

Though PredPol\u2019s efforts to leverage technology to make communities safer is admirable in theory, critics have expressed concern about the potential for algorithmic discrimination, ethical issues, and sociological implications. PredPol claims that no demographic, ethnic or socioeconomic information is ever used in its algorithm, thus eliminating privacy or civil rights violations [7]. However, this claim is predicated on the assumption that the data supporting the algorithm is also free of demographic, ethnic or socioeconomic information. In an article from statistics magazine Significance<\/em>, researchers Kristin Lum and William Isaac argue, \u201cusing predictive policing algorithms to deploy police resources would result in the disproportionate policing of low-income communities and communities of color.\u201d [8]. Leslie Gordon, a contributor to the American Bar Association Journal, agrees. She raises concerns about the potential for this technology to violate the fourth amendment and further stigmatize neighborhoods that are already heavily patrolled [9]. For the Los Angeles Police Department, an early adopter of predictive policing (using PredPol and services from other firms like Palantir), the controversy reached a turning point this summer. The Stop LAPD Spying Coalition released a report this past May critiquing the department\u2019s data policing techniques and alleging that the data is biased against blacks and Latinos. The department uses multiple machine learning tools that employ targeting strategies that are people-based and place-based. The current police chief, Michel Moore, supports an inspector general audit of the existing practices to improve relations between his force and the community [10].<\/p>\n

As techniques in machine learning and predictive policing evolve and allegations of algorithmic bias persist, PredPol\u2019s management will need to consider how to manage risk, avoid misuse of its technology, and evaluate the potential for bias in its existing algorithm. I offer two questions that merit further reflection on this topic: What measures should be taken from a governance perspective to ensure that PredPol\u2019s predictive policing algorithm is fair and unbiased? Should citizens be alerted when predictive policing algorithms are implemented in their communities?<\/p>\n

[791 words]<\/p>\n

Exhibit 1<\/u><\/strong><\/p>\n

Source: Law Enforcement at PredPol<\/a><\/p>\n

\"\"<\/a><\/p>\n

Predictions displayed as red boxes on a web interface via Google Maps.<\/p>\n

\"\"<\/a>
Dosage refers to the amount of time officers spend in PredPol boxes<\/figcaption><\/figure>\n
\"\"<\/a>
PredPol\u2019s reporting module.<\/figcaption><\/figure>\n

References<\/u><\/strong><\/p>\n

[1] RAND Corporation (2013).\u00a0Predictive Policing: Forecasting Crime for Law Enforcement<\/em>. [online] Santa Monica. Available at: https:\/\/www.rand.org\/pubs\/research_briefs\/RB9735.html. [Accessed 12 Nov. 2018].<\/p>\n

[2]\u00a0Weisburd, D. and Neyroud, P. (2011). Police science: Toward a new paradigm. In:\u00a0Executive Session on Policing and Public Safety<\/em>. [online] National Institute of Justice. Available at: https:\/\/www.ncjrs.gov\/pdffiles1\/nij\/228922.pdf [Accessed 12 Nov. 2018].<\/p>\n

[3] Crunchbase. (2018).\u00a0PredPol Overview<\/em>. [online] Available at: https:\/\/www.crunchbase.com\/organization\/predpol#section-overview [Accessed 12 Nov. 2018].<\/p>\n

[4]\u00a0PredPol. (2018).\u00a0The Three Pillars of Predictive Policing<\/em>. [online] Available at: http:\/\/www.predpol.com\/law-enforcement\/ [Accessed 12 Nov. 2018].<\/p>\n

[5]\u00a0Smith, M. (2018). Can we predict when and where crime will take place?.\u00a0BBC News<\/em>. [online] Available at: https:\/\/www.bbc.com\/news\/business-46017239 [Accessed 12 Nov. 2018].<\/p>\n

[6] Attacking the Opioid Overdose Epidemic One Prediction at a Time. (2018). [Blog]\u00a0PredPol Blog<\/em>. Available at: http:\/\/blog.predpol.com\/attacking-the-opioid-overdose-epidemic-one-prediction-at-a-time [Accessed 12 Nov. 2018].<\/p>\n

[7] PredPol (2018).\u00a0Science and Testing of Predictive Policing<\/em>. White Paper. [online] PredPol. Available at: http:\/\/www.predpol.com\/how-predictive-policing-works\/ [Accessed 12 Nov. 2018].<\/p>\n

[8] Lum, K. and Isaac, W. (2016). To predict and serve?.\u00a0Significance<\/em>, 13(5), pp.14-19.<\/p>\n

[9] Gordon, L. (2013). A Byte Out of Crime: Predictive policing may help bag burglars\u2014but it may also be a constitutional problem.\u00a0ABA Journal<\/em>, [online] 99(9). Available at: https:\/\/www.jstor.org\/stable\/i24595886 [Accessed 12 Nov. 2018].<\/p>\n

[10] Chang, C. (2018). LAPD officials defend predictive policing.\u00a0LA Times<\/em>. [online] Available at: http:\/\/www.latimes.com\/local\/lanow\/la-me-lapd-data-policing-20180724-story.html [Accessed 12 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"

Law enforcement agencies are using machine learning tools to analyze, track, and attempt to predict crime. <\/p>\n","protected":false},"author":11914,"featured_media":33366,"comment_status":"open","ping_status":"closed","template":"","categories":[4627,4819,1355,346,2677,4820,4839],"class_list":["post-33214","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artificial-intellgience","category-civil-liberties","category-ethics","category-machine-learning","category-predictive-analytics","category-predictive-policing","category-predpol","hck-taxonomy-organization-predpol","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nMachine Learning at PredPol: Risks, Biases, and Opportunities for Predictive Policing - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-at-predpol-risks-biases-and-opportunities-for-predictive-policing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning at PredPol: Risks, Biases, and Opportunities for Predictive Policing - 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