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Adaptive Sampling to Reduce Disparate Performance

Jun 11, 2020
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang


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A Notion of Individual Fairness for Clustering

Jun 08, 2020
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern


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Diversity and Inclusion Metrics in Subset Selection

Feb 09, 2020
Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern

* AIES 2020: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 

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Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information

Jun 07, 2019
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern


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FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

Apr 10, 2019
Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau

* Under review 

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Fair Dimensionality Reduction and Iterative Rounding for SDPs

Feb 28, 2019
Jamie Morgenstern, Samira Samadi, Mohit Singh, Uthaipon Tantipongpipat, Santosh Vempala


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Predictive Inequity in Object Detection

Feb 21, 2019
Benjamin Wilson, Judy Hoffman, Jamie Morgenstern


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Guarantees for Spectral Clustering with Fairness Constraints

Jan 24, 2019
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern


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Fair k-Center Clustering for Data Summarization

Jan 24, 2019
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern


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The Price of Fair PCA: One Extra Dimension

Oct 31, 2018
Samira Samadi, Uthaipon Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh Vempala


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Datasheets for Datasets

Jul 09, 2018
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, Kate Crawford

* Working Paper, comments are encouraged 

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A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Jan 10, 2018
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu


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Fairness in Reinforcement Learning

Aug 06, 2017
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

* The short version of this paper appears in the proceedings of ICML-17 

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Fair Algorithms for Infinite and Contextual Bandits

Jun 29, 2017
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth


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A Convex Framework for Fair Regression

Jun 07, 2017
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth


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Fairness in Learning: Classic and Contextual Bandits

Nov 07, 2016
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

* A condensed version of this work appears in the 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 

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Do Prices Coordinate Markets?

Jun 22, 2016
Justin Hsu, Jamie Morgenstern, Ryan Rogers, Aaron Roth, Rakesh Vohra


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Learning Simple Auctions

Apr 11, 2016
Jamie Morgenstern, Tim Roughgarden


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