Fairness of Exposure in Rankings.

Rankings are ubiquitous in the online world today. As we have transitionedfrom finding books in libraries to ranking products, jobs, job applicants,opinions and potential romantic partners, there is a substantial precedent thatranking systems have a responsibility not only to their users but also to theitems being ranked. To address these often conflicting responsibilities, wepropose a conceptual and computational framework that allows the formulation offairness constraints on rankings. As part of this framework, we developefficient algorithms for finding rankings that maximize the utility for theuser while satisfying fairness constraints for the items. Since fairness goalscan be application specific, we show how a broad range of fairness constraintscan be implemented in our framework, including forms of demographic parity,disparate treatment, and disparate impact constraints. We illustrate the effectof these constraints by providing empirical results on two ranking problems.