{"id":500,"date":"2021-04-12T16:36:42","date_gmt":"2021-04-12T20:36:42","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-peopleanalytics\/submission\/recidivism-risk-algorithms\/"},"modified":"2021-04-12T16:44:49","modified_gmt":"2021-04-12T20:44:49","slug":"predicting-crime","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-peopleanalytics\/submission\/predicting-crime\/","title":{"rendered":"Predicting Crime"},"content":{"rendered":"

Berten Verbeeck & Cem Pektas<\/strong><\/p>\n

Recidivism risk algorithms<\/em><\/p>\n

\u00a0<\/em><\/p>\n

The prevalence of algorithmic assessment tools has been growing rapidly in recent years. Across industries, organizations have been developing use cases for predictive analytical models, ranging from determining your car insurance premium to setting your credit score.<\/p>\n

 <\/p>\n

Many of these practices are now well established. Yet, in 2016, major controversy was stirred when ProPublica published an investigation into the use of recidivism risk algorithms in America\u2019s criminal justice system (https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing<\/a>). In their article, ProPublica\u2019s researchers lay out how they analyzed data from a widely used recidivism risk assessment tool called COMPAS (Correctional Offender Management Profiling for Alternative Sanctions).<\/p>\n

 <\/p>\n

The idea behind COMPAS is simple: Have artificial intelligence process enormous quantities of data on issues such as age, sex, employment, current criminal charge, number of past convictions, etc. to provide advice on whether or not a specific individual will commit another offense in the future. Proponents of the system have argued that it helps judges and parole officers make better-informed, data-driven decisions. However, ProPublica\u2019s investigation claims that COMPAS is biased against African Americans. In their article, the authors state: \u201cBlack defendants were often predicted to be at a higher risk of recidivism than they actually were. Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent vs. 23 percent).\u201d<\/p>\n

 <\/p>\n

While some later researchers have criticized ProPublica\u2019s methodology \u2013 and have argued that algorithm-based tools vastly outperform humans in predicting recidivism (see, for example, https:\/\/news.berkeley.edu\/2020\/02\/14\/algorithms-are-better-than-people-in-predicting-recidivism-study-says\/<\/a>) \u2013 there is no doubt that systems such as COMPAS risk perpetuating established biases. When considering the context of America\u2019s criminal justice system, this is particularly worrisome for two reasons. First, the stakes are incredibly high, with individuals\u2019 most fundamental possession \u2013 their freedom \u2013 in the balance. Second, America\u2019s criminal justice system has a well-documented history of racial bias, and it is not unthinkable that African Americans \u2013 when evaluated based on an algorithm \u2013 may find themselves discriminated against once again due to overrepresentation in past arrest data.<\/p>\n

 <\/p>\n

This does not mean we have to do away with recidivism risk algorithms altogether. Tools such as COMPAS can be valuable in highly complex cases, where vast numbers of factors \u2013 more than the human mind can process \u2013 need to be taken into account. Yet, two fundamental conditions need to be put in place before authorizing its use. First, the algorithm should never replace judges. It can be leveraged as one input into a judge\u2019s overall decision making \u2013 a complement to existing methods \u2013 but can never be allowed to make decisions independent of any human. Second, any judge who uses the algorithm must be able adequately interpret and explain its workings. A judge must be able to determine when certain situational factors are not considered by the algorithm, and then be capable to overwrite the algorithm\u2019s recommendations. When a judge does decide to follow the algorithm\u2019s advice, he\/she must be able to spell out in clear language what drove the decision-making. As per the Fifth Amendment, any defendant has the right to \u201cdue process of law\u201d. This includes the right to understand how one\u2019s algorithmic score was calculated and the right to challenge the score\u2019s accuracy. Blindly following an algorithm would mean depriving defendants of their right to due process.<\/p>\n","protected":false},"excerpt":{"rendered":"

Recidivism risk algorithms – Should AI decide who goes free and who goes to jail?<\/p>\n","protected":false},"author":18857,"featured_media":507,"comment_status":"open","ping_status":"closed","template":"","categories":[465,443,489,487,491,430],"class_list":["post-500","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-ai","category-algorithms","category-biases","category-decision-making","category-discrimination","category-justice-system","hck-taxonomy-organization-united-states-criminal-justice-system","hck-taxonomy-industry-public-administration","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-peopleanalytics\/assignment\/lpa-2021-blog-assignment\/","yoast_head":"\nPredicting Crime - Leading with People Analytics<\/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-peopleanalytics\/submission\/predicting-crime\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting Crime - 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