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