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Working Papers

Recent Working Papers by Optimizing Government Project collaborators

Richard Berk, Arun Kuchibhotla & Eric Tchetgen, Fair Risk Algorithms, 10 Annu. Rev. Stat. Appl. 165 (2023).

Cary Coglianese, Regulating Machine Learning: The Challenge of Heterogeneity (2023).

Richard Berk, Arun Kuchibhotla & Eric Tchetgen, Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets (2022).

Cary Coglianese, Moving Towards Personalized Law (2022).

Cary Coglianese & Kat Hefter, From Negative to Positive Algorithm Rights, 30 Wm. & Mary Bill Rts. J 883 (2022).

Cary Coglianese & Alicia Lai, Assessing Automated Administration (2022).

Cary Coglianese & Alicia Lai, Algorithm vs. Algorithm, 72 Duke L.J. 1281 (2022).

Cary Coglianese & Alicia Lai, Antitrust by Algorithm, 2 Stanford Computational Antitrust 1 (2022).

Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth & Saeed Sharifi-Malvajerdi, Multiaccurate Proxies for Downstream Fairness (2022).

Richard Berk, Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement, 4 Ann. Rev. Criminology 209 (2021).

Cary Coglianese, Administrative Law in the Automated State, 150 Daedalus 104 (2021).

Cary Coglianese, Regulating New Tech: Problems, Pathways, and People (2021).

Cary Coglianese & Lavi Ben Dor, AI in Adjudication and Administration, 86 Brook. L. Rev. 791 (2021).

Cary Coglianese & Erik Lampmann, Contracting for Algorithmic Accountability, 6 Admin. L. Rev. Accord 175 (2021).

Arun Kuchibhotla & Richard Berk, Nested Conformal Prediction Sets for Classification with Applications to Probation Data (2021).

Cary Coglianese, Deploying Machine Learning for a Sustainable Future (2020).

Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth & Rakesh Vohra, Fair Prediction with Endogenous Behavior (2020).

Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton & Zhiwei Steven Wu, An Algorithmic Framework for Fairness Elicitation (2020).

Michael Kearns & Aaron Roth, Ethical algorithm design should guide technology regulation (2020).

Richard Berk, Machine Learning Risk Assessments in Criminal Justice Settings (2019).

Richard Berk & Susan Sorenson, An Algorithmic Approach to Forecasting Rare Violent Events: An Illustration Based in IPV Perpetration (2019).

Cary Coglianese & David Lehr, Transparency and Algorithmic Governance, 71 Admin. L. Rev. 1 (2019).

Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi & Jonathan Ullman, Differentially Private Fair Learning (2019).

Michael Kearns, Aaron Roth & Saeed Sharifi-Malvajerdi, Average Individual Fairness: Algorithms, Generalization and Experiments (2019).

Richard Berk, Hoda Heidaric, Shahin Jabbaric, Michael Kearns & Aaron Roth, Fairness in Criminal Justice Risk Assessments: The State of the Art (2018).

Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth & Zachary Schutzman, Fair Algorithms for Learning in Allocation Problems (2018).

Sampath Kannan, Aaron Roth & Juba Ziani, Downstream Effects of Affirmative Action (2018).

Michael Kearns, Data Intimacy, Machine Learning, and Consumer Privacy (2018).

Tom Baker & Benedict Dellaert, Regulating Robo Advice Across the Financial Services Industry (2017). 

Richard Berk, Accuracy and Fairness for Juvenile Justice Risks Assessments (2017).

Cary Coglianese & David Lehr, Regulating by Robot: Administrative Decision-Making in the Machine-Learning Era, 105 Geo. L.J. 1147 (2017). 

Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh Pai, Aaron Roth, Rakesh Vohra & Z. Steven Wu, Fairness Incentives for Myopic Agents (2017).

Richard Berk, Susan B. Sorenson & Geoffrey Barnes, Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016). 

Cary Coglianese, Optimizing Government for an Optimizing Economy, University of Pennsylvania Institute for Law and Economics (2016). 

Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern & Aaron Roth, Fair Learning in Markovian Environments (2016). 

Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel & Aaron Roth, Rawlsian Fairness for Machine Learning (2016). 

Matthew Joseph, Michael Kearns, Jamie Morgenstern & Aaron Roth, Fairness in Learning: Classic and Contextual Bandits (2016).

Michael Kearns, Aaron Roth, Zhiwei Steven Wu & Grigory Yaroslavtsev, Privacy for the Protected (Only) (2015).

Other Recent Publications by Optimizing Government Project collaborators

Richard Berk & Jordan Hyatt, Machine Learning Forecasts of Risk to Inform Sentencing Decisions, 27 Federal Sentencing Reporter 222 (2015). 

Richard Berk, Criminal Justice Forecasts of Risk (2012).

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