Notable research on the use of machine learning in government, policy-making, and regulation. Please email regulation@law.upenn.edu<\/a> if you would like to submit additional sources.<\/p>\n\n\n\n General Interest<\/a><\/strong><\/p>\n\n\n\n Challenges<\/a><\/strong><\/p>\n\n\n\n Applications<\/a><\/strong><\/p>\n\n\n\n Methods<\/a><\/strong><\/p>\n\n\n\n U.S. Executive Office of the President, Preparing for the Future of Artificial Intelligence<\/a> (2016). <\/p>\n\n\n\n U.S. Executive Office of the President, The Administration\u2019s Report on the Future of Artificial Intelligence<\/a> <\/em>(2016). <\/p>\n\n\n\n U.S. Executive Office of the President, Big Data: Seizing Opportunities, Preserving Values<\/a><\/em> (2014). <\/p>\n\n\n\n U.S. Government Accountability Office, Data Mining: Federal Efforts Cover a Wide Range of Uses<\/a>,<\/em> (May 2004).<\/p>\n\n\n\n U.S. Joint ad hoc Committee of the Committee on Adjudication and Committee on Administration and Management, Electronic Case Management in Federal Administrative Adjudication<\/a>,<\/em> (June 2018).<\/p>\n\n\n\n U.K. House of Commons, Science and Technology Committee, Robotics and Artificial Intelligence<\/a> <\/em>(2016). <\/p>\n\n\n\n A.I. Now Institute, A.I. Now 2017 Report<\/a> (2017). <\/p>\n\n\n\n L. Anastasopoulos & Andrew Whitford, Machine Learning for Public Administration Research with Application to Organizational Reputation,<\/a><\/em> (2018).<\/p>\n\n\n\n Oren Bar-Gill, Algorithimic Price Discrimination When Demand is a Function of Both Preferences and (Mis)Perceptions<\/a>,<\/em> U. Chicago L. Rev. (forthcoming 2018).<\/p>\n\n\n\n Solon Barocas, Ethics and Policy in Data Science<\/a>, course syllabus (2017). <\/p>\n\n\n\n Solon Barocas et al. Governing Algorithms: A Provocation Piece<\/a>,<\/em> (presented at the Governing Algorithms Conference, 2013).<\/p>\n\n\n\n Stuart Minor Benjamin, Algorithms and Speech<\/a><\/em>, 161 U. Pa. L. Rev. 1445 (2013).<\/p>\n\n\n\n Sarah Bird, Solon Barocas, Kate Crawford, et al., Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in A<\/a>I<\/em> (Oct. 2016). <\/p>\n\n\n\n Nick Bostrom, Superintelligence: Paths, Dangers, Strategies<\/a> (2014). <\/p>\n\n\n\n danah boyd & Kate Crawford, Six Provocations for Big Data<\/a><\/em> (presented at A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, 2011).<\/p>\n\n\n\n John Brockman (editor), What to Think About Machines That Think<\/a> (2015).<\/p>\n\n\n\n Erik Brynjolfsson & Andrew McAffee, The Second Machine Age<\/a> (2014). <\/p>\n\n\n\n Mark Buchanan, Our Robot Overlords Might be Delayed,<\/a><\/em> Bloomberg (2018).<\/p>\n\n\n\n Ryan Calo, Robotics and the Lessons of Cyberlaw<\/a>, 103 Calif. L. Rev. 513 (2015).<\/p>\n\n\n\n Centre for Public Impact, How to Make AI Work in Government and for People<\/a>,<\/em> (2018).<\/p>\n\n\n\n Centre for Public Impact, Destination Unknown: Exploring the Impact of Artificial Intelligence on Government<\/a>,<\/em>(2017).<\/p>\n\n\n\n Cary Coglianese, Optimizing Government for an Optimizing Economy<\/a><\/em>, University of Pennsylvania Institute for Law and Economics (June 2016). <\/p>\n\n\n\n Cary Coglianese & David Lehr, Regulating by Robot: Administrative Decision-Making in the Machine-Learning Era<\/a>, 105 Geo. L.J. 1147 (2017). <\/p>\n\n\n\n Mariano-Florentino Cu\u00e9llar, The Surprising Use of Automation by Regulatory Agencies<\/a>, <\/em>The Regulatory Review (2016).<\/p>\n\n\n\n Deloitte Center for Government Insights, Using Advanced Analytics to Drive Regulatory Reform<\/a>,<\/em> (2017).<\/p>\n\n\n\n Kevin C. Desouza, Delivering Artificial Intelligence in Government: Challenges and Opportunities<\/a> <\/em>(2018).<\/p>\n\n\n\n Megan Rose Dickey, Algorithmic Accountability<\/a>, Techcrunch.com (April 30, 2017).<\/p>\n\n\n\n Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World<\/a> (2015).<\/p>\n\n\n\n The Return of the Machinery Question<\/a><\/em>, The Economist (June 25, 2016).<\/p>\n\n\n\n Lilian Edwards & Michael Veale, Enslaving the Algorithm: From a \u2018Right to an Explanation\u2019 to a \u2018Right to Better Decisions\u2019?<\/a> (Oct. 31, 2017). <\/p>\n\n\n\n Lilian Edwards & Michael Veale, Slave to the Algorithm? Why a \u2018Right to an Explanation\u2019 is Probably Not the Remedy You are Looking For<\/a>, Duke L. & Tech. J. (forthcoming 2017). <\/p>\n\n\n\n Ed Felten, Accountable Algorithms<\/a><\/em>, Freedom to Tinker (Sep. 12, 2012).<\/p>\n\n\n\n Martin Ford, Rise of the Robots: Technology and the Threat of a Jobless Future<\/a> <\/em>(2015). <\/p>\n\n\n\n Dirk Helbing et al., Will Democracy Survive Big Data and Artificial Intelligence?<\/a><\/em>, Scientific American (Feb. 25, 2017).<\/p>\n\n\n\n Dan Hurley, Can an Algorithm Tell When Kids Are in Danger?<\/a>, N.Y. Times (Jan. 2, 2018). <\/p>\n\n\n\n Henry Kissinger, How the Enlightenment Ends<\/a>,<\/em> The Atlantic (June 2018).<\/p>\n\n\n\n IBM Center for the Business of Government, The Future Has Begun: Using Artificial Intelligence to Transform Government,<\/a><\/em> (2017).<\/p>\n\n\n\n Joshua A. Kroll et al., Accountable Algorithms<\/em>,<\/a> 165 U. Pa. L. Rev. 633 (2017).<\/p>\n\n\n\n John Kamensky, Artificial Intelligence and the Future of Government<\/a>,<\/em> Government Executive (2018).<\/p>\n\n\n\n Jon Kleinberg et al., Human Decisions and Machine Predictions <\/em><\/a>(National Bureau of Economic Research Working Paper No. 23180, 2017).<\/p>\n\n\n\n Will Knight, The Dark Secret at the Heart of AI<\/a>, MIT Technology Review (April 11, 2017). <\/p>\n\n\n\n Rory Van Loo, Rise of the Digital Regulator<\/a>, 66 Duke L. J. 1267 (2017).<\/p>\n\n\n\n John Markoff, Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots<\/a> (2015).<\/p>\n\n\n\n Viktor Mayer-Sch\u00f6nberger & Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think<\/a> (2013).<\/p>\n\n\n\n Cathy O\u2019Neil, How Can We Stop Algorithms Telling Lies?<\/a>, The Guardian (July 16, 2017). <\/p>\n\n\n\n Cathy O\u2019Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy<\/a> (2016).<\/p>\n\n\n\n Joshua New & Daniel Castro, How Policymakers Can Foster Algorithmic Accountability,<\/a><\/em> Information Technology & Innovation Foundation (2018).<\/p>\n\n\n\n New York University Information Law Institute, Algorithms and Explanations<\/a><\/em> (April 27-28, 2017) (panel slides).<\/p>\n\n\n\n Nextgov, Disrupting Government<\/a>,<\/em> 2018.<\/p>\n\n\n\n Partnership for Public Service, The Future Has Begun: Using Artificial Intelligence to Transform Government<\/a> <\/em>(2018). <\/p>\n\n\n\n Vijay Pande, Artificial Intelligence\u2019s \u2018Black Box\u2019 is Nothing to Fear<\/a>, N.Y. Times (Jan. 25, 2018). <\/p>\n\n\n\n W. N. Price II, Regulating Black-Box Medicine<\/a>, <\/em>116 Mich. L. Rev. 421 (2017).<\/p>\n\n\n\n Andrew Selbst & Solon Barocas, Regulating Inscrutable Systems<\/a>, <\/em>(2018).<\/p>\n\n\n\n Cassidy R. Sugimoto, Hamid R. Ekbia & Michael Mattioli (editors), Big <\/em><\/a>Data<\/a><\/em> is Not a Monolith<\/em><\/a> (2016). <\/p>\n\n\n\n Michael Veale, Logics and Practices of Transparency and Opacity in Real-World Applications of Public Sector Machine Learning<\/a>, Remarks at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (Aug. 14, 2017). <\/p>\n\n\n\n University of Michigan Center on Finance, Law, and Policy, Big Data <\/a>in<\/a> Finance<\/a> (Oct. 27-28, 2016) (panel videos and notes). <\/p>\n\n\n\n Upturn and Omidyar Network, Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods<\/a>,<\/em>(2018).<\/p>\n\n\n\n Li Zhou, Is Your Software Racist?<\/em><\/a>, Politico (Feb. 8, 2018).<\/p>\n\n\n\n <\/a>Equal Protection and Bias<\/em><\/strong><\/p>\n\n\n\n Solon Barocas & Andrew Selbst, Big Data\u2019s Disparate Impact<\/a><\/em>, 104 Cal. L. Rev 671 (2016). <\/p>\n\n\n\n Nanette Byrnes, Why We Should Expect Algorithms to Be Biased<\/a><\/em>, MIT Tech. Rev. (June 24, 2016).<\/p>\n\n\n\n Cynthia Dwork, et al., Fairness Through Awareness<\/a><\/em> (Nov. 29, 2011).<\/p>\n\n\n\n Sorelle Friedler et al., Certifying and Removing Disparate Impact<\/a><\/em> (presented at Fairness, Accountability, and Transparency in Machine Learning, 2014).<\/p>\n\n\n\n Aziz Huq, Racial Equity in Algorithmic Criminal Justice<\/a>,<\/em> Duke L. Journal (2019).<\/p>\n\n\n\n Toshihiro Kamishima et al., Fairness-Aware Classifier with Prejudice Remover Regularizer<\/a>, in Machine Learning and Knowledge Discovery in Databases<\/em> (Peter Flach, Tijl De Bie & Nello Cristianini eds., 2012).<\/p>\n\n\n\n Will Knight, Biased Algorithms are Everywhere, and No One Seems to Care<\/a>, MIT Technology Review (July 12, 2017). <\/p>\n\n\n\n Hannah Laqueur & Ryan Copus, Synthetic Crowdsourcing: A Machine-Learning Approach to Problems of Inconsistency and Bias in Adjudication<\/a> (Oct. 21, 2016). <\/p>\n\n\n\n Adam Liptak, Sent to Prison by a Software Program\u2019s Secret Algorithms<\/a>, N.Y. Times (May 1, 2017). <\/p>\n\n\n\n Claire Cain Miller, Algorithms and Bias: Q. and A. With Cynthia Dwork<\/a><\/em>, N.Y. Times (Aug. 10, 2015).<\/p>\n\n\n\n Katherine Noyes, Will Big Data Help End Discrimination\u2014or Make It Worse?<\/a><\/em>, Fortune (Jan. 15, 2015). <\/p>\n\n\n\n Michael Schrage, Big Data\u2019s Dangerous New Era of Discrimination<\/a><\/em>, Harv. Bus. Rev. (Jan. 29, 2014).<\/p>\n\n\n\n Sonja Starr, Evidence-Based Sentencing and the Scientific Rationalization of Discrimination<\/a><\/em>, 66 Stan. L. Rev. 804 (2014).<\/p>\n\n\n\n Latanya Sweeney, Discrimination in Online Ad Delivery<\/a><\/em>, 56 Comms. of the Ass\u2019n for Computer Machinery 44 (May 2013).<\/p>\n\n\n\n Laura Sydell, Can Computers Be Racist? The Human-Like Bias of Algorithms<\/a><\/em>, NPR (March 14, 2016).<\/p>\n\n\n\n Kim Zetter, Researchers Sue the Government Over Computer Hacking Law<\/a><\/em>, Wired (July 29, 2016).<\/p>\n\n\n\n Privacy<\/em><\/strong><\/p>\n\n\n\n Steven M. Bellovin et al., When Enough is Enough: Location Tracking, Mosaic Theory, and Machine Learning<\/a><\/em>, 8 N.Y.U. J. L. & Liberty 555 (2014).<\/p>\n\n\n\n Kate Crawford & Jason Schultz, Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms<\/a><\/em>, 55 B.C. L. Rev. 93 (2014).<\/p>\n\n\n\n Roger Allan Ford & W. Nicholson Price II, Privacy and Accountability in Black-Box Medicine<\/a><\/em>, 22 Mich. Telecomm. & Tech. L. Rev. (forthcoming 2016).<\/p>\n\n\n\n Neil Richards & Jonathan King, Three Paradoxes of Big Data<\/a><\/em>, 66 Stan. L. Rev. Online 41 (2013).<\/p>\n\n\n\n Omer Tene & Jules Polonestky, \n
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\n\n\n\nGeneral Interest<\/h3>\n\n\n\n
\n\n\n\nChallenges<\/h2>\n\n\n\n