FairSight: Visual Analytics for Fairness in Decision Making
Open accessAbstract
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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A framework exists that visually analyzes bias in a model for both individual and group fairness, with fairness reports shown like XAI visual summaries.
“For example, [398] proposes a framework to visually analyze the bias present in a model (both for individual and group fairness). Thus, the fairness report is shown just like the visual summaries used within XAI.”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [398]
Cites
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“…such as the What-If Tool (Wexler et al., 2020), Cap-tum (Kokhlikyan et al., 2019), Manifold (Zhang et al., 2018), or InterpretML (Nori et al., 2019), while others focus on specific applications like fairness, including FairVis (Cabrera et al., 2019) and FairSight (Ahn and Lin, 2019).”
BackgroundExploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
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A survey on datasets for fairness‐aware machine learning
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- Authors
- Ahn, Y., & Lin, Y.-R.
- Affiliations
- University of Pittsburgh (US)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2019
- Volume
- 26
- Issue
- 1
- Pages
- 1-1
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W2969670093
- Cited by
- 126
- References
- 3
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 24 · 2023: 23 · 2022: 24 · 2021: 24 · 2020: 10 · 2019: 2
- FWCI
- 26.22
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (#1637067, #1739413)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic fairness, visual analytics, data-driven decision making, ranking fairness, bias mitigation, fairness metrics, discrimination
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