Fairness of Exposure in Rankings
This work addresses fairness concerns for items being ranked in online platforms, such as products or job applicants, by providing a flexible computational framework, though it is incremental in building on existing fairness concepts.
The paper tackles the problem of balancing user utility and fairness in ranking systems by proposing a framework that formulates fairness constraints in terms of exposure allocation, and it develops efficient algorithms to maximize utility while provably satisfying specifiable fairness notions, with empirical results demonstrated on two ranking problems.
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 in terms of exposure allocation. As part of this framework, we develop efficient algorithms for finding rankings that maximize the utility for the user while provably satisfying a specifiable notion of fairness. Since fairness goals can be application specific, we show how a broad range of fairness constraints can be implemented using 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.