Recent development on distributed systems has shown that a variety of fairness constraints (some of which are only recently defined) play vital roles in designing self-stabilizing population protocols. Current practice of system analysis is, however, deficient under fairness. In this work, we present PAT, a toolkit for flexible and efficient system analysis under fairness. A unified algorithm i...

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness me...

It is widely agreed that exams must be fair; yet what this exactly means is not made clear. One may mean fairness of treatment, but this merely propagates the fairness or unfairness of pre-existing rules. Fairness of opportunity on the other hand necessarily leads to identical grades for everyone, which clearly makes it inapplicable. Neither view is helpful to make decisions on competing claims...