no code implementations • 12 May 2022 • Rina Friedberg, Karthik Rajkumar, Jialiang Mao, Qian Yao, YinYin Yu, Min Liu
By leveraging prior experimentation, we obtain quasi-experimental variation in item rankings that is orthogonal to user relevance.
no code implementations • 15 Feb 2022 • YinYin Yu, Guillaume Saint-Jacques
In this paper, we derive an algorithmic fairness metric from the fairness notion of equal opportunity for equally qualified candidates for recommendation algorithms commonly used by two-sided marketplaces.
no code implementations • 15 Nov 2021 • Jonathan Roth, Guillaume Saint-Jacques, YinYin Yu
This paper extends Becker (1957)'s outcome test of discrimination to settings where a (human or algorithmic) decision-maker produces a ranked list of candidates.