Richard Berk, Arun Kuchibhotla & Eric Tchetgen, Fair Risk Algorithms<\/em><\/a>, 10 Annu. Rev. Stat. Appl. 165 (2023). <\/p>\n\n\n\n Cary Coglianese, Regulating Machine Learning: The Challenge of Heterogeneity<\/em><\/a> (2023).<\/p>\n\n\n\n Richard Berk, Arun Kuchibhotla & Eric Tchetgen, Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets<\/a><\/em> (2022).<\/p>\n\n\n\n Cary Coglianese, Moving Towards Personalized Law<\/a> <\/em>(2022).<\/p>\n\n\n\n Cary Coglianese & Kat Hefter, From Negative to Positive Algorithm Rights<\/em><\/a>, 30 Wm. & Mary Bill Rts. J 883 (2022).<\/p>\n\n\n\n Cary Coglianese & Alicia Lai, Assessing Automated Administration<\/a> <\/em>(2022).<\/p>\n\n\n\n Cary Coglianese & Alicia Lai, Algorithm vs. Algorithm<\/a>, <\/em>72 Duke L.J. 1281 (2022).<\/p>\n\n\n\n Cary Coglianese & Alicia Lai, Antitrust by Algorithm<\/a>, <\/em>2 Stanford Computational Antitrust 1 (2022).<\/p>\n\n\n\n Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth & Saeed Sharifi-Malvajerdi, Multiaccurate Proxies for Downstream Fairness<\/a><\/em> (2022).<\/p>\n\n\n\n Richard Berk, Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement<\/a><\/em>, 4 Ann. Rev. Criminology 209 (2021).<\/p>\n\n\n\n Cary Coglianese, Administrative Law in the Automated State<\/em><\/a>, 150 Daedalus 104 (2021).<\/p>\n\n\n\n Cary Coglianese, Regulating New Tech: Problems, Pathways, and People<\/a><\/em> (2021).<\/p>\n\n\n\n Cary Coglianese & Lavi Ben Dor, AI in Adjudication and Administration<\/a><\/em>, 86 Brook. L. Rev. 791 (2021).<\/p>\n\n\n\n Cary Coglianese & Erik Lampmann, Contracting for Algorithmic Accountability<\/a><\/em>, 6 Admin. L. Rev. Accord 175 (2021).<\/p>\n\n\n\n Arun Kuchibhotla & Richard Berk, Nested Conformal Prediction Sets for Classification with Applications to Probation Data<\/a><\/em> (2021). <\/p>\n\n\n\n Cary Coglianese, Deploying Machine Learning for a Sustainable Future<\/a><\/em> (2020).<\/p>\n\n\n\n Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth & Rakesh Vohra, Fair Prediction with Endogenous Behavior<\/a><\/em> (2020).<\/p>\n\n\n\n Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton & Zhiwei Steven Wu, An Algorithmic Framework for Fairness Elicitation<\/a><\/em> (2020).<\/p>\n\n\n\n Michael Kearns & Aaron Roth, Ethical algorithm design should guide technology regulation<\/a><\/em> (2020).<\/p>\n\n\n\n Richard Berk, Machine Learning Risk Assessments in Criminal Justice Settings<\/em><\/a> (2019). <\/p>\n\n\n\n Richard Berk & Susan Sorenson, An Algorithmic Approach to Forecasting Rare Violent Events: An Illustration Based in IPV Perpetration<\/a><\/em> (2019). <\/p>\n\n\n\n Cary Coglianese & David Lehr, Transparency and Algorithmic Governance<\/a><\/em>, 71 Admin. L. Rev. 1 (2019).<\/p>\n\n\n\n Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi & Jonathan Ullman, Differentially Private Fair Learning<\/a><\/em> (2019). <\/p>\n\n\n\n Michael Kearns,\u00a0Aaron Roth &\u00a0Saeed Sharifi-Malvajerdi, Average Individual Fairness: Algorithms, Generalization and Experiments<\/a><\/em> (2019).<\/p>\n\n\n\n Richard Berk, Hoda Heidaric, Shahin Jabbaric, Michael Kearns & Aaron Roth, Fairness in Criminal Justice Risk Assessments: The State of the Art<\/em><\/a> (2018).<\/p>\n\n\n\n Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth & Zachary Schutzman, Fair Algorithms for Learning in Allocation Problems<\/a><\/em> (2018).<\/p>\n\n\n\n Sampath Kannan, Aaron Roth & Juba Ziani, Downstream Effects of Affirmative Action<\/em><\/a> (2018).<\/p>\n\n\n\n Michael Kearns, Data Intimacy, Machine Learning, and Consumer Privacy<\/a><\/em> (2018).<\/p>\n\n\n\n Tom Baker & Benedict Dellaert, Regulating Robo Advice Across the Financial Services Industry<\/a> <\/em>(2017). <\/p>\n\n\n\n Richard Berk, Accuracy and Fairness for Juvenile Justice Risks Assessments<\/a><\/em> (2017).<\/p>\n\n\n\n Cary Coglianese & David Lehr, Regulating by Robot: Administrative Decision-Making in the Machine-Learning Era<\/a><\/em>, 105 Geo. L.J. 1147 (2017). <\/p>\n\n\n\n Sampath Kannan,\u00a0Michael Kearns,\u00a0Jamie Morgenstern,\u00a0Mallesh Pai,\u00a0Aaron Roth,\u00a0Rakesh Vohra &\u00a0Z. Steven Wu, Fairness Incentives for Myopic Agents<\/a><\/em> (2017).<\/p>\n\n\n\n Richard Berk, Susan B. Sorenson & Geoffrey Barnes, Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions<\/a><\/em> (2016). <\/p>\n\n\n\n Cary Coglianese, Optimizing Government for an Optimizing Economy<\/a><\/em>, University of Pennsylvania Institute for Law and Economics (2016). <\/p>\n\n\n\n Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern & Aaron Roth, Fair Learning in Markovian Environments<\/em><\/a> (2016). <\/p>\n\n\n\n Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel & Aaron Roth, Rawlsian Fairness for Machine Learning<\/a><\/em> (2016). <\/p>\n\n\n\n Matthew Joseph, Michael Kearns, Jamie Morgenstern & Aaron Roth,\u00a0Fairness in Learning: Classic and Contextual Bandits<\/a><\/em>\u00a0(2016).<\/p>\n\n\n\n Michael Kearns, Aaron Roth, Zhiwei Steven Wu & Grigory Yaroslavtsev, Privacy for the Protected (Only)<\/a><\/em> (2015).<\/p>\n\n\n\n Richard Berk & Jordan Hyatt, Machine Learning Forecasts of Risk to Inform Sentencing Decisions<\/a><\/em>, 27 Federal Sentencing Reporter 222 (2015). <\/p>\n\n\n\n
\n\n\n\nOther Recent Publications by Optimizing Government Project collaborators<\/h3>\n\n\n\n