FairSight: Visual Analytics for Fairness in Decision Making
FairSight: Visual Analytics for Fairness in Decision Making
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DOI：
10.1109/tvcg.2019.2934262
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发表时间：
2020-01-01
影响因子：
5.2
通讯作者：
Lin, Yu-Ru
中科院分区：
文献类型：
--
作者：
Ahn, Yongsu;Lin, Yu-Ru
与个人相关的数据驱动决策越来越普遍，但最近的研究提出了有关潜在歧视的问题。作为回应，研究人员努力提出并实施公平措施和算法，但这些努力尚未转化为数据驱动决策的现实实践。因此，仍然迫切需要创造一种可行的工具来促进公平决策。我们提出FairSight，一个视觉分析系统来解决这一需求；它的目的是通过确定所需的行动——理解、衡量、诊断和减轻偏见——来实现对决策公平的不同概念，这些行动共同导致更公平的决策。通过案例研究和用户研究，我们证明了系统中的可视化分析和诊断模块在理解公平感知决策管道和获得更公平的结果方面是有效的。
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.