{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:52Z","timestamp":1750220452072,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            Given the increased concern of racial disparities in the stop-and-frisk programs, the\n            <jats:bold>New York Police Department<\/jats:bold>\n            (\n            <jats:bold>NYPD<\/jats:bold>\n            ) requires publicly displaying detailed data for all the stops conducted by police authorities, including the suspected offense and race of the suspects. By adopting a public data transparency policy, it becomes possible to investigate racial biases in stop-and-frisk data and demonstrate the benefit of data transparency to approve or disapprove social beliefs and police practices. Thus, data transparency becomes a crucial need in the era of\n            <jats:bold>Artificial Intelligence<\/jats:bold>\n            (\n            <jats:bold>AI<\/jats:bold>\n            ), where police and justice increasingly use different AI techniques not only to understand police practices but also to predict recidivism, crimes, and terrorism. In this study, we develop a predictive analytics method, including bias metrics and bias mitigation techniques to analyze the NYPD Stop-and-Frisk datasets and discover whether underline bias patterns are responsible for stops and arrests. In addition, we perform a fairness analysis on two protected attributes, namely, the race and the gender, and investigate their impacts on arrest decisions. We also apply bias mitigation techniques. The experimental results show that the NYPD Stop-and-Frisk dataset is not biased toward colored and Hispanic individuals and thus law enforcement authorities can apply the bias predictive analytics method to inculcate more fair decisions before making any arrests.\n          <\/jats:p>","DOI":"10.1145\/3460533","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T13:09:20Z","timestamp":1644584960000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Data Transparency and Fairness Analysis of the NYPD Stop-and-Frisk Program"],"prefix":"10.1145","volume":"14","author":[{"given":"Youakim","family":"Badr","sequence":"first","affiliation":[{"name":"The Pennsylvania State University, Great Valley, Malvern, PA, USA"}]},{"given":"Rahul","family":"Sharma","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, Great Valley, Malvern, PA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1162\/rest.91.1.163"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0049124118782533"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/11925231_23"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1086\/684292"},{"key":"e_1_3_1_10_2","unstructured":"New York City Police Department. 2012. The NYPD Stop Question and Frisk Database. Retrieved from December 8 2021. https:\/\/www1.nyc.gov\/site\/nypd\/stats\/reports-analysis\/stopfrisk.page."},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aao5580"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOAS897"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.45"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294834"},{"key":"e_1_3_1_18_2","unstructured":"N. Mehrabi F. Morstatter N. Saxena K. Lerman and A. Galstyan. 2019. A Survey on bias and fairness in machine learning. arXiv:1908.09635[Cs]."},{"key":"e_1_3_1_19_2","unstructured":"M. Michael Marjorie Grynbaum and Connelly. 2012. Majority in City See Police as Favoring Whites Poll Finds. Retrieved from December 8 2021. https:\/\/www.nytimes.com\/2012\/08\/21\/nyregion\/64-of-new-yorkers-in-poll-say-police-favor-whites.html."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1080\/00220670209598786"},{"key":"e_1_3_1_21_2","unstructured":"G Ridgeway. 2007. Analysis of racial disparities in the New York Police Department\u2019s stop question and frisk practices. RAND Corporation. Retrieved from December 8 2021. https:\/\/www.rand.org\/pubs\/technical_reports\/TR534.html."},{"key":"e_1_3_1_22_2","unstructured":"Ray Rivera. 2012. Pockets of City See Higher Use of Force During Police Stops. Retrieved from December 8 2021. https:\/\/www.nytimes.com\/2012\/08\/16\/nyregion\/in-police-stop-data-pockets-where-force-is-used-more-often.html."},{"key":"e_1_3_1_23_2","first-page":"1","article-title":"The enduring discriminatory practice of stop-and-frisk: An analysis of stop-and-frisk policing","author":"Stolper Harold","year":"2018","unstructured":"Harold Stolper and Jeff Jones. 2018. The enduring discriminatory practice of stop-and-frisk: An analysis of stop-and-frisk policing. NYC Community Service Society (2018), 1\u20139.","journal-title":"NYC Community Service Society"},{"key":"e_1_3_1_24_2","unstructured":"H. Suresh and J. V. Guttag. 2020. A framework for understanding unintended consequences of machine learning http:\/\/arxiv.org\/abs\/1901.10002."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3194770.3194776"},{"key":"e_1_3_1_26_2","volume-title":"Stop and Frisk: Balancing Crime Control with Community Relations","author":"Vigne Nancy G. La","year":"2014","unstructured":"Nancy G. La Vigne, Pamela Lachman, Shebani Rao, and Andrea Matthews. 2014. Stop and Frisk: Balancing Crime Control with Community Relations. Office of Community Oriented Policing Services, Washington, DC. 978\u2013979."},{"key":"e_1_3_1_27_2","first-page":"5666","article-title":"Probably approximately metric-fair learning","author":"Yona G.","year":"2018","unstructured":"G. Yona and G. Rothblum. 2018. Probably approximately metric-fair learning. In International Conference on Machine Learning, 5666\u20135674.","journal-title":"International Conference on Machine Learning"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.5555\/3042817.3042973"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34041-3_27"}],"container-title":["Journal of Data and Information Quality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460533","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3460533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:56Z","timestamp":1750193276000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460533"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,6,30]]}},"alternative-id":["10.1145\/3460533"],"URL":"https:\/\/doi.org\/10.1145\/3460533","relation":{},"ISSN":["1936-1955","1936-1963"],"issn-type":[{"type":"print","value":"1936-1955"},{"type":"electronic","value":"1936-1963"}],"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2020-12-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}