FairSight: Visual Analytics for Fairness in Decision Making

By Yongsu Ahn, Yu-Ru Lin. 2019

149 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science, AI Ethics, Fairness & Society, Human-Computer Interaction

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “For example, [398] proposes a framework to visually analyze the bias present in a model (both for individual and group fairness).”
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020, background): “…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).”
Explainable artificial intelligence: a comprehensive review (2021, methodology): “For example, Ahn and Lin (2019) applied a ranking mechanism in order to achieve model-agnostic post-modeling explainability for various prediction approaches, which included binary and multiclass classification.”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “Ahn and Lin [4] present a framework and visual analytic design to aid fair data-driven decision making.”
Robustness Gym: Unifying the NLP Evaluation Landscape (2021, unknown): “, 2019].”