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.
Probability Paths and the Structure of Predictions over Time
With Zhiyuan (Jerry) Lin and Hao Sheng. Working paper.
[ Supplementary Information ]
Blind Justice: Algorithmically Masking Race in Charging Decisions
With Alex Chohlas-Wood, Joe Nudell, Zhiyuan (Jerry) Lin, and Julian Nyarko. Working paper.
A Causal Framework for Observational Studies of Discrimination
With Johann Gaebler, William Cai, Guillaume Basse, Ravi Shroff, and Jennifer Hill. Working paper.
[ Code ]
Breaking Taboos in Fair Machine Learning: An Experimental Study
With Julian Nyarko and Roseanna Sommers. Working paper.
[ Commentary
in the Boston Globe ]
Bandit Algorithms to Personalize Educational Chatbots
With William Cai, Joshua Grossman, Zhiyuan (Jerry) Lin, Hao Sheng,
Johnny Tian-Zheng Wei, and Joseph Jay Williams. Working paper.
The Accuracy, Equity, and Jurisprudence of Criminal Risk Assessment
With Ravi Shroff, Jennifer Skeem, and Christopher Slobogin.
Research Handbook on Big Data Law (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, Vol. 183, 2020.
[ Commentary
in Harvard Business Review ]
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, Vol. 4, 2020.
[
Stanford Open Policing Project -
Supporting Information -
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, Vol. 117, 2020.
[ Listen to audio samples
- Data & Code ]
The Limits of Human Predictions of Recidivism
With Zhiyuan (Jerry) Lin, Jongbin Jung, and Jennifer Skeem.
Science Advances, Vol. 6, 2020.
[ Commentary in The Washington Post -
Data & Code ]
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, Vol. 114, 2020.
[ Commentary in Slate -
Interview on This American Life ]
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).
[ Awarded Best Paper at 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).
[ Awarded Best Paper at 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 (Jerry) 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)
[ Awarded Best Paper at 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 in New York City's Stop-and-Frisk Policy
With Justin Rao and Ravi Shroff.
Annals of Applied Statistics, Vol. 10, 2016.
[ Processed stop-and-frisk data as an RData file; original
NYPD data. ]
The Effect of Recommendations on Network Structure
With Jessica Su and Aneesh Sharma.
Proceedings of the 25th International World Wide Web Conference (WWW 2016).
The Structural Virality of Online Diffusion
With Ashton Anderson, Jake Hofman, and Duncan J. Watts.
Management Science, Vol. 62, 2016.
Forecasting Elections with Non-Representative Polls
With Wei Wang, David Rothschild, and Andrew Gelman.
International Journal of Forecasting, Vol 31, 2015.
Political Ideology and Racial Preferences in Online Dating
With Ashton Anderson, Gregory Huber, Neil Malhotra, and Duncan J. Watts.
Sociological Science, Vol. 1, 2014.
[
Rejoinder to a
comment on our paper. ]
Predicting Individual Behavior with Social Networks
With Daniel G. Goldstein.
Marketing Science, Vol. 33, 2014.
Sharding Social Networks
With Quang Duong, Jake Hofman, and Sergei Vassilvitskii.
Proceedings of the Fifth Conference on Web Search and Data Mining (WSDM 2012).
Respondent Driven Sampling—Where We Are and Where Should We be Going?
With Richard White, Amy Lansky, David Wilson, Wolfgang Hladik, Avi Hakim and Simon DW Frost
Sexually Transmitted Infections, Vol. 88, No. 6, 2012, 397-399.
[ Supporting Information ]
The Structure of Online Diffusion Networks
With Duncan J. Watts and Daniel G. Goldstein.
Proceedings of the 13th ACM Conference on Economics & Computation (EC 2012).
Who Does What on the Web: Studying Web Browsing Behavior at Scale
With Jake Hofman and M. Irmak Sirer
Proceedings of the 6th International Conference on Weblogs and Social Media (ICWSM 2012).
Predicting Consumer Behavior with Web Search
With Jake Hofman, Sébastien Lahaie, David Pennock, and Duncan Watts
Proceedings of the National Academy of Sciences, Vol 107, No. 41, 2010, 17486-17490.
Real and Perceived Attitude Agreement in Social Networks
With Winter Mason and Duncan Watts
Journal of Personality and Social Psychology, Vol. 99, No. 4, 2010, 611-621.
Assessing Respondent-Driven Sampling
With Matthew Salganik
Proceedings of the National Academy of Sciences, Vol. 107, No. 15, 2010, 6743-6747.
[ Supporting Information -
Project 90 Data ]
Prediction Without Markets
With Daniel Reeves, Duncan Watts, and David Pennock
Proceedings of the 11th ACM Conference on Economics & Computation (EC 2010).
Anatomy of the Long Tail: Ordinary People With Extraordinary Tastes
With Andrei Broder, Evgeniy Gabrilovich, and Bo Pang
Proceedings of the Third Conference on Web Search and Data Mining (WSDM 2010).
Contract Auctions for Sponsored Search
With Sébastien Lahaie and Sergei Vassilvitskii
Proceedings of the 5th Workshop on Internet and Network Economics (WINE 2009).
Short version in SIGecom Exchanges
Collective Revelation:
A Mechanism for Self-Verified, Weighted, and Truthful Predictions
With Daniel Reeves and David Pennock
Proceedings of the 10th ACM Conference on Economics & Computation (EC 2009).
CentMail: Rate Limiting via Certified Micro-Donations
With Jake Hofman, John Langford, David Pennock, and Daniel Reeves
Proceedings of the 6th Conference on Email and Anti-Spam (CEAS 2009).
[ Short version at WWW 2009, Developer's Track ]
Respondent-Driven Sampling as Markov Chain Monte Carlo
With Matthew Salganik
Statistics in Medicine, Vol. 28, No. 17, 2009, 2202-2229.
Social Search in “Small-World” Experiments
With Roby Muhamad and Duncan Watts
Proceedings of the 18th International World Wide Web Conference (WWW 2009).
Predictive Indexing for Fast Search
With John Langford and Alex Strehl
Advances in Neural Information Processing Systems (NIPS 2008).
Yoopick: A Combinatorial Sports Prediction Market
With David Pennock, Daniel Reeves, and Cong Yu
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008).
Pricing Combinatorial Markets for Tournaments
With Yiling Chen and David Pennock
Proceedings of the 40th ACM Symposium on Theory of Computing (STOC 2008).
Horseshoes in Multidimensional Scaling and Local Kernel Methods
With Persi Diaconis and Susan Holmes
Annals of Applied Statistics, Vol. 2, No. 3, 2008, 777-807.
An Invisible Minority: Asian-Americans in Mathematics
Notices of the American Mathematical Society, Vol. 53, No. 8, 2006, 878-882.
Analysis of Top to Bottom-k Shuffles
Annals of Applied Probability, Vol. 16, No. 1, 2006, 30-55.
Mixing Time Bounds via the Spectral Profile
With Ravi Montenegro and Prasad Tetali
Electronic Journal of Probability, Vol. 11, 2006, 1-26.
Eluding Carnivores: File Sharing with Strong Anonymity
With Emin Gün Sirer, Mark Robson, and Doğan Engin
Proceedings of the 11th ACM SIGOPS European Workshop. 2004.
Modified Logarithmic Sobolev Inequalities for Some Models of Random Walk
Stochastic Processes and Their Applications, Vol. 114, 2004, 51-79.