Recent Working Papers by Optimizing Government Project collaborators
Richard Berk, Hoda Heidaric, Shahin Jabbaric, Michael Kearns, & Aaron Roth, Fairness in Criminal Justice Risk Assessments: The State of the Art (January 2018).
Tom Baker & Benedict Dellaert, Regulating Robo Advice Across the Financial Services Industry (March 2017).
Cary Coglianese, Optimizing Government for an Optimizing Economy, University of Pennsylvania Institute for Law and Economics (June 2016).
Cary Coglianese and David Lehr, Regulating by Robot: Administrative Decision-Making in the Machine-Learning Era, 105 Geo. L.J. 1147 (2017).
Cary Coglianese and David Lehr, Transparency and Algorithmic Governance, 71 Admin. L. Rev. 1 (2019).
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel & Aaron Roth, Rawlsian Fairness for Machine Learning (Nov. 2016).
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern & Aaron Roth, Fair Learning in Markovian Environments (Nov. 2016).
Matthew Joseph, Michael Kearns, Jamie Morgenstern & Aaron Roth, Fairness in Learning: Classic and Contextual Bandits (May 2016).
Richard Berk, Susan B. Sorenson & Geoffrey Barnes, Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (March 2016).
Other Recent Publications by Optimizing Government Project collaborators
Richard Berk, Criminal Justice Forecasts of Risk (2012).
Richard Berk and Jordan Hyatt, Machine Learning Forecasts of Risk to Inform Sentencing Decisions, 27 Federal Sentencing Reporter 222 (2015).