no code implementations • 12 Dec 2021 • Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian Ziebart
While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race.
1 code implementation • 11 Oct 2020 • Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian Ziebart
We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.
1 code implementation • 10 Mar 2019 • Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian Ziebart
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications.
no code implementations • NeurIPS 2018 • Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.
no code implementations • 20 Dec 2017 • Hong Wang, Ashkan Rezaei, Brian D. Ziebart
Many predicted structured objects (e. g., sequences, matchings, trees) are evaluated using the F-score, alignment error rate (AER), or other multivariate performance measures.