FairSight: Visual Analytics for Fairness in Decision Making
This addresses the need for practical tools to facilitate fair decision making in real-world applications, though it appears incremental as it builds on existing fairness measures and algorithms.
The authors tackled the problem of discrimination in data-driven decision making by developing FairSight, a visual analytics system that helps users understand, measure, diagnose, and mitigate biases to achieve fairness in ranking decisions, and demonstrated its effectiveness through case and user studies.
Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies. In response, researchers have made efforts to propose and implement fairness measures and algorithms, but those efforts have not been translated to the real-world practice of data-driven decision making. As such, there is still an urgent need to create a viable tool to facilitate fair decision making. We propose FairSight, a visual analytic system to address this need; it is designed to achieve different notions of fairness in ranking decisions through identifying the required actions -- understanding, measuring, diagnosing and mitigating biases -- that together lead to fairer decision making. Through a case study and user study, we demonstrate that the proposed visual analytic and diagnostic modules in the system are effective in understanding the fairness-aware decision pipeline and obtaining more fair outcomes.