FairSight: Visual Analytics for Fairness in Decision Making

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...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics Jg. 26; H. 1; S. 1086 - 1095
Hauptverfasser: Ahn, Yongsu, Lin, Yu-Ru
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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, 1941-0506
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Zusammenfassung: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.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2019.2934262