1 code implementation • 31 Jan 2023 • Omid Memarrast, Linh Vu, Brian Ziebart
The fairness of machine learning-based decisions has become an increasingly important focus in the design of supervised machine learning methods.
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 Dec 2020 • Daniel Khashabi, Arman Cohan, Siamak Shakeri, Pedram Hosseini, Pouya Pezeshkpour, Malihe Alikhani, Moin Aminnaseri, Marzieh Bitaab, Faeze Brahman, Sarik Ghazarian, Mozhdeh Gheini, Arman Kabiri, Rabeeh Karimi Mahabadi, Omid Memarrast, Ahmadreza Mosallanezhad, Erfan Noury, Shahab Raji, Mohammad Sadegh Rasooli, Sepideh Sadeghi, Erfan Sadeqi Azer, Niloofar Safi Samghabadi, Mahsa Shafaei, Saber Sheybani, Ali Tazarv, Yadollah Yaghoobzadeh
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
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.