9 citations · 10 across the 4 of their papers we have counts for
4 papers
Causal Estimation of Position Bias in Recommender Systems Using Marketplace Instruments
Rina Friedberg, Karthik Rajkumar, Jialiang Mao +3
Information retrieval systems, such as online marketplaces, news feeds, and search engines, are ubiquitous in today's digital society. They facilitate information discovery by rank…
Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity
Cyrus DiCiccio, Brian Hsu, YinYin Yu +2
Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected…
Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn
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 b…
An Outcome Test of Discrimination for Ranked Lists
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. Ranked lists are p…