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Daniel J. Weitzner and Randall Davis. Rothbacher, Nicolas S. Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Technology and Policy Program. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Institute for Data, Systems, and Society Technology and Policy Program Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Engineering Systems Division n-us-ny 2021-01-06T18:30:58Z 2021-01-06T18:30:58Z 2020 2020 https://hdl.handle.net/1721.1/129138 1227276615 Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, September, 2020 Thesis: S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 79-84). Predictive policing has quickly become widespread in the United States. Practitioners claim it can greatly increase police efficiency and base decisions on objective statistics. Critics say that these algorithms reproduce discriminatory outcomes in a biased justice system. In this thesis, I investigate fare enforcement in New York City and what might happen if predictive policing were applied. First I analyze legal precedents on discrimination law to create a framework for understanding whether policy is legally discriminatory. In this framework the fairness of a government policy is judged based on how different groups are treated by the process of carrying out the policy. Three elements must be examined: a comparison group that is treated fairly, discriminatory burden for the disadvantaged group, and government negligence or intent. Next, using this framework, I perform data analysis on fare evasion arrests in New York City, and find evidence of discrimination. Finally, I examine predictive policing to determine what its effect on fare enforcement might be. I conclude that predictive policing algorithms trained on the arrests will be ineffective and seen as unfair due to the institutional practices that impact the data. This examination sheds light on how machine learning fairness could be analyzed using societal expectations of fairness. by Nicolas S. Rothbacher. S.M. in Technology and Policy S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science S.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program S.M.MassachusettsInstituteofTechnology,DepartmentofElectricalEngineeringandComputerScience 84 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Institute for Data, Systems, and Society. Technology and Policy Program. Electrical Engineering and Computer Science. Discrimination, fairness and prediction in policing : fare evasion in New York City Thesis application/pdf 2021-01-06T18:30:57Z Publication Master TPP ESD IDSS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="1546b542-572a-4b45-920d-777d7d56ace6"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Discrimination, fairness and prediction in policing : fare evasion in New York City</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Rothbacher, Nicolas S.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Institute for Data, Systems, and Society.</Keyword> <Keyword>Technology and Policy Program.</Keyword> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Predictive policing has quickly become widespread in the United States. Practitioners claim it can greatly increase police efficiency and base decisions on objective statistics. Critics say that these algorithms reproduce discriminatory outcomes in a biased justice system. In this thesis, I investigate fare enforcement in New York City and what might happen if predictive policing were applied. First I analyze legal precedents on discrimination law to create a framework for understanding whether policy is legally discriminatory. In this framework the fairness of a government policy is judged based on how different groups are treated by the process of carrying out the policy. Three elements must be examined: a comparison group that is treated fairly, discriminatory burden for the disadvantaged group, and government negligence or intent. Next, using this framework, I perform data analysis on fare evasion arrests in New York City, and find evidence of discrimination. Finally, I examine predictive policing to determine what its effect on fare enforcement might be. I conclude that predictive policing algorithms trained on the arrests will be ineffective and seen as unfair due to the institutional practices that impact the data. This examination sheds light on how machine learning fairness could be analyzed using societal expectations of fairness.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>