FairSight: Visual Analytics for Fairness in Decision Making

• Published in: IEEE transactions on visualization and computer graphics Vol. 26; no. 1; pp. 1086 - 1095
• Main Authors: Ahn, Yongsu, Lin, Yu-Ru
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Decision analysis; Decision making; Diagnostic systems; Fairness in Machine Learning; Machine learning; Machine learning algorithms; Mathematical analysis; Pipelines; Task analysis; Visual Analytic; Visual analytics
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2019.2934262

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.