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The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective



Satyapriya Krishna , Tessa Han , Alex Gu , Javin Pombra , Shahin Jabbari , Steven Wu , Himabindu Lakkaraju


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Towards the Unification and Robustness of Perturbation and Gradient Based Explanations



Sushant Agarwal , Shahin Jabbari , Chirag Agarwal , Sohini Upadhyay , Zhiwei Steven Wu , Himabindu Lakkaraju


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Active Screening for Recurrent Diseases: A Reinforcement Learning Approach



Han-Ching Ou , Haipeng Chen , Shahin Jabbari , Milind Tambe

* The short version of this paper appears in the proceedings of AAMAS-21 

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Fair Influence Maximization: A Welfare Optimization Approach



Aida Rahmattalabi , Shahin Jabbari , Himabindu Lakkaraju , Phebe Vayanos , Eric Rice , Milind Tambe


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Equilibrium Characterization for Data Acquisition Games



Jinshuo Dong , Hadi Elzayn , Shahin Jabbari , Michael Kearns , Zachary Schutzman

* The short version of this paper appears in the proceedings of IJCAI-19 

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Fair Algorithms for Learning in Allocation Problems



Hadi Elzayn , Shahin Jabbari , Christopher Jung , Michael Kearns , Seth Neel , Aaron Roth , Zachary Schutzman


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Fairness in Reinforcement Learning



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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A Convex Framework for Fair Regression



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


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Fairness in Criminal Justice Risk Assessments: The State of the Art



Richard Berk , Hoda Heidari , Shahin Jabbari , Michael Kearns , Aaron Roth

* Under a Revise and Resubmit 

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