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Causal Estimation of Position Bias in Recommender Systems Using Marketplace Instruments
By leveraging prior experimentation, we obtain quasi-experimental variation in item rankings that is orthogonal to user relevance.
Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn
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.
An Outcome Test of Discrimination for Ranked Lists
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.
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