论文标题
机器学习中歧视的视觉分析
Visual Analysis of Discrimination in Machine Learning
论文作者
论文摘要
在关键应用中，诸如犯罪预测和大学录取等关键应用程序中对自动决策的使用日益增长提出了有关机器学习公平性的疑问。我们如何决定不同的治疗方法是合理的还是歧视性的？在本文中，我们从视觉分析的角度研究了机器学习中的歧视，并提出了一种交互式可视化工具，distilens，以支持更全面的分析。为了揭示有关算法歧视的详细信息，DICLENS根据因果建模和分类规则挖掘了一系列潜在的歧视性项目集。通过将扩展的Euler图与基于矩阵的可视化相结合，我们开发了一种新颖的设置可视化，以促进歧视性项目集的探索和解释。一项用户研究表明，用户可以快速准确地解释视觉编码的信息。用例表明，DISTILERS为理解和减少算法歧视提供了信息指导。
The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination.
