footprints.stanford.eduSharad Goel - Assistant Professor at Stanford

Assistant Professor @ Stanford About Teaching Research Hello! I’m an assistant professor at Stanford in the Department of Management Science & Engineering , in the School of Engineering . I also have courtesy appointments in Computer Science , Sociology , and the Law School . My primary area of research is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. I’m particularly interested in applying modern computational and statistical techniques to understand and improve public policy. Some topics I’ve recently worked on are: stop-and-frisk , tests for racial bias , fair machine learning , swing voting , election polls , voter fraud , filter bubbles , and online privacy . I’m the founder and executive director of the Stanford Computational Policy Lab , a team of researchers, data scientists, and journalists that addresses policy problems through technical innovation. In collaboration with the Computational Journalism Lab , we created the Stanford Open Policing Project , a repository of data on over 100 million traffic stops across the United States. I often write essays about contemporary policy issues from a statistical perspective. These include discussions of algorithms in the courts (in the New York Times and the Washington Post ); policing (in Slate and The Huffington Post ); election polls (in the New York Times ); claims of voter fraud (in Slate , and also an extended interview with This American Life ); and affirmative action (in Boston Review ). I studied at the University of Chicago (B.S. in Mathematics) and at Cornell (M.S. in Computer Science; Ph.D. in Applied Mathematics ). Before joining the Stanford faculty, I worked at Microsoft Research in New York City. If you would like to chat, please stop by my office ( Huang 251 ), or send me an email . Selected Publications [ Google Scholar ] The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning With Sam Corbett-Davies. Working paper. Omitted and Included Variable Bias in Tests for Disparate Impact With Sam Corbett-Davies, Jongbin Jung, and Ravi Shroff. Working paper. MathBot: A Personalized Conversational Agent for Learning Math With William Cai, Joshua Grossman, Zhiyuan Lin, Hao Sheng, Johnny Tian-Zheng Wei, and Joseph Jay Williams. Working paper. A Large-scale Analysis of Racial Disparities in Police Stops Across the United States With Emma Pierson, Camelia Simoiu, Jan Overgoor, Sam Corbett-Davies, Daniel Jenson, Amy Shoemaker, Vignesh Ramachandran, Phoebe Barghouty, Ravi Shroff, and Cheryl Phillips. Nature Human Behaviour (Conditionally Accepted). [ Stanford Open Policing Project - Commentary in Slate ] Racial Disparities in Automated Speech Recognition With Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John Rickford, and Dan Jurafsky. Proceedings of the National Academy of Sciences (Forthcoming). Simple Rules to Guide Expert Classifications With Jongbin Jung, Connor Concannon, Ravi Shroff, and Daniel G. Goldstein. Journal of the Royal Statistical Society: Series A (Forthcoming). [ Commentary in Harvard Business Review ] The Limits of Human Predictions of Recidivism With Zhiyuan Lin, Jongbin Jung, and Jennifer Skeem. Science Advances (Forthcoming). One Person, One Vote: Estimating the Prevalence of Double Voting in U.S. Presidential Elections With M. Meredith, M. Morse, D. Rothschild, and H. Shirani-Mehr. American Political Science Review (Forthcoming). [ Commentary in Slate - Interview on This American Life ] The Accuracy, Equity, and Jurisprudence of Criminal Risk Assessment With Ravi Shroff, Jennifer Skeem, and Christopher Slobogin. Research Handbook on Big Data Law (Forthcoming). Fair Allocation through Selective Information Acquisition With William Cai, Johann Gaebler, and Nikhil Garg. Conference on AI, Ethics, and Society (AIES 2020). Bayesian Sensitivity Analysis for Offline Policy Evaluation With Jongbin Jung, Ravi Shroff, and Avi Feller. Conference on AI, Ethics, and Society (AIES 2020). Partisan Selective Exposure in Online News Consumption: Evidence from the 2016 Presidential Campaign With Erik Peterson and Shanto Iyengar. Political Science Research and Methods, 2020. An Experimental Study of Structural Diversity in Social Networks With Jessica Su, Krishna Kamath, Aneesh Sharma, and Johan Ugander. The 14th International Conference On Web and Social Media (ICWSM 2020). Studying the “Wisdom of Crowds” at Scale With Camelia Simoiu, Chiraag Sumanth, and Alok Mysore. The 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2019). “I was told to buy a software or lose my computer. I ignored it”: A study of ransomware With Camelia Simoiu, Christopher Gates, and Joseph Bonneau. Fifteenth Symposium on Usable Privacy and Security (SOUPS 2019). Guiding Prosecutorial Decisions with an Interpretable Statistical Model With Zhiyuan Lin and Alex Chohlas-Wood. Conference on AI, Ethics, and Society (AIES 2019). Machine Learning, Health Disparities, and Causal Reasoning With Steven Goodman and Mark Cullen. Annals of Internal Medicine, Vol. 169, 2018. Disentangling Bias and Variance in Election Polls With Houshmand Shirani-Mehr, David Rothschild, and Andrew Gelman. Journal of the American Statistical Association, Vol. 113, 2018. [ Commentary in The New York Times ] Fast Threshold Tests for Detecting Discrimination With Emma Pierson and Sam Corbett-Davies. The 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) Creating Crowdsourced Research Talks at Scale With Rajan Vaish, Shirish Goyal, and Amin Saberi. Proceedings of the 27th International World Wide Web Conference (WWW 2018). [ Video clip - Stanford Scholar ] Online, Opt-in Surveys: Fast and Cheap, but are they Accurate? With Adam Obeng and David Rothschild. Technical Report, 2017. Crowd Research: Open and Scalable University Laboratories With Rajan Vaish, Michael S. Bernstein, et al. Proceedings of the 30th Annual Symposium on User Interface Software and Technology (UIST 2017). Algorithmic Decision Making and the Cost of Fairness With Sam Corbett-Davies, Emma Pierson, Avi Feller, and Aziz Huq. Proceedings of the 23rd Conference on Knowledge Discovery and Data Mining (KDD 2017). [ Commentary in New York Times - Commentary in Washington Post - Tutorial on fair ML ] The Problem of Infra-marginality in Outcome Tests for Discrimination With Camelia Simoiu and Sam Corbett-Davies. Annals of Applied Statistics, Vol. 11, 2017. [ Data - code ] De-Anonymizing Web Browsing Data with Social Networks With Ansh Shukla, Jessica Su, and Arvind Narayanan. Proceedings of the 26th International World Wide Web Conference (WWW 2017). [ Commentary in Slate ] Combatting Police Discrimination in the Age of Big Data With Maya Perelman, Ravi Shroff, and David Sklansky. New Criminal Law Review, Vol. 20, 2017. [ Commentary in The Huffington Post ] Understanding Emerging Threats to Online Advertising With Ceren Budak, Justin Rao, and Georgios Zervas. Proceedings of the 17th ACM Conference on Economics & Computation (EC 2016). Personalized Risk Assessments in the Criminal Justice System With Justin Rao and Ravi Shroff. The American Economic Review: Papers and Proceedings, Vol. 106, 2016. High-Frequency Polling with Non-Representative Data With Andrew Gelman, David Rothschild, and Wei Wang. Routledge Studies in Global Information, Politics and Society, 2016. The Mythical Swing Voter With David Rothschild, Andrew Gelman, and Doug Rivers. Quarterly Journal of Political Science, Vol. 11, 2016. Filter Bubbles, Echo Chambers, and Online News Consumption With Seth Flaxman and Justin Rao. Public Opinion Quarterly, Vol. 80, 2016. [ Supporting Information ] Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis With Ceren Budak and Justin Rao. Public Opinion Quarterly, Vol. 80, 2016. Precinct or Prejudice? Understanding Racial Disparities...