2020

Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu (2020) Metric-Free Individual Fairness in Online Learning. NeurIPS 2020.

Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, Ankit Sharma (2020). Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries. Oper. Res. 68(1): 16-34.

Avrim Blum, Thodoris Lykouris (2020). Advancing Subgroup Fairness via Sleeping Experts. ITCS: 55:1-55:24

Avrim Blum, Kevin Stangl (2020). Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? FORC: 3:1-3:20

Alexandra Chouldechova, Aaron Roth (2020) A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5): 82-89

Kate Donahue, Jon M. Kleinberg (2020) Fairness and utilization in allocating resources with uncertain demand. FAT*: 658-668

Cynthia Dwork, Christina Ilvento, Meena Jagadeesan (2020) Individual Fairness in Pipelines. FORC: 7:1-7:22

Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, Pragya Sur (2020) Abstracting Fairness: Oracles, Metrics, and Interpretability. FORC: 8:1-8:16

Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis (2020) A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. AISTATS: 4463-4473

Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra (2020) Fair Prediction with Endogenous Behavior. EC: 677-678

Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona (2020) Preference-informed fairness. FAT*: 546

Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona (2020) Preference-informed fairness. ITCS: 16:1-16:23

Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy (2020) Mitigating bias in algorithmic hiring: evaluating claims and practices. FAT*: 469-481

Selected earlier publications

Rediet Abebe, Jon M. Kleinberg, David C. Parkes (2017) Fair Division via Social Comparison. AAMAS: 281-289

Amihood Amir, Oren Kapah, Tsvi Kopelowitz, Moni Naor, Ely Porat (2016) The Family Holiday Gathering Problem or Fair and Periodic Scheduling of Independent Sets. SPAA: 367-375

Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu (2019) Equal Opportunity in Online Classification with Partial Feedback. NeurIPS: 8972-8982

Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis (2018) Efficient Statistics, in High Dimensions, from Truncated Samples. FOCS: 639-649

Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis (2019) Computationally and Statistically Efficient Truncated Regression. COLT: 955-960

Cynthia Dwork (2017) What’s Fair? KDD: 1

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel (2012) Fairness through awareness. ITCS: 214-226

Cynthia Dwork, Christina Ilvento (2019) Fairness Under Composition. ITCS: 33:1-33:20

Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Mark D. M. Leiserson (2018) Decoupled Classifiers for Group-Fair and Efficient Machine Learning. FAT: 119-133

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona (2019) Learning from Outcomes: Evidence-Based Rankings. FOCS: 106-125

Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman (2019) Fair Algorithms for Learning in Allocation Problems. FAT: 170-179

Sumegha Garg, Michael P. Kim, Omer Reingold (2019) Tracking and Improving Information in the Service of Fairness. EC: 809-824

Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth (2018) Online Learning with an Unknown Fairness Metric. NeurIPS: 2605-2614

Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum (2018) Multicalibration: Calibration for the (Computationally-Identifiable) Masses. ICML: 1944-1953

Nicole Immorlica, Katrina Ligett, and Juba Ziani (2019) Access to Population-Level Signaling as a Source of Inequality. FAT*

Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth (2017) Fairness in Reinforcement Learning. ICML: 1617-1626

Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman (2019) Differentially Private Fair Learning. ICML: 3000-3008

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth (2018) Meritocratic Fairness for Infinite and Contextual Bandits. AIES: 158-163

Matthew Joseph, Michael J. Kearns, Jamie H. Morgenstern, Aaron Roth (2016) Fairness in Learning: Classic and Contextual Bandits. NIPS: 325-333

Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro (2018) On preserving non-discrimination when combining expert advice. NeurIPS: 8386-8397 EC 2017: 369-386

Sampath Kannan, Aaron Roth, Juba Ziani (2019) Downstream Effects of Affirmative Action. FAT: 240-248

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu (2018) Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML: 2569-2577

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu (2019) An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT: 100-109

Michael P. Kim, Omer Reingold, Guy N. Rothblum (2018) Fairness Through Computationally-Bounded Awareness. NeurIPS: 4847-4857

Jon M. Kleinberg (2018) Inherent Trade-Offs in Algorithmic Fairness. SIGMETRICS (Abstracts): 40

Jon M. Kleinberg, Sendhil Mullainathan (2019) Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability. EC: 807-808

Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan (2017) Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS: 43:1-43:23

Jon M. Kleinberg, Sigal Oren, Manish Raghavan (2016) Planning Problems for Sophisticated Agents with Present Bias. EC: 343-360

Jon M. Kleinberg, Sigal Oren, Manish Raghavan (2017) Planning with Multiple Biases. EC: 567-584

Jon M. Kleinberg, Manish Raghavan (2018) Selection Problems in the Presence of Implicit Bias. ITCS: 33:1-33:17

Moni Naor. (2005) On fairness in the carpool problem. J. Algorithms 55(1): 93-98

Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger (2017) On Fairness and Calibration. NIPS: 5680-5689

Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth (2019) Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS: 8240-8249

Gal Yona, Guy N. Rothblum (2018) Probably Approximately Metric-Fair Learning. ICML: 5666-5674

Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork (2013) Learning Fair Representations. ICML (3): 325-333