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.

翻译：在犯罪预测和大学录取等关键应用中，自动化决策的日益普及引发了关于机器学习公平性的问题。我们如何判断不同的处理方式是合理还是具有歧视性？本文从可视分析角度研究机器学习中的歧视问题，并提出交互式可视分析工具DiscriLens，以支持更全面的分析。为揭示算法歧视的详细信息，DiscriLens基于因果建模和分类规则挖掘识别一组潜在歧视性项集。通过将扩展的欧拉图与基于矩阵的可视化相结合，我们开发了一种新颖的集合可视化方法，以促进歧视性项集的探索与解读。用户研究表明，用户能够快速准确地理解DiscriLens中视觉编码的信息。用例表明，DiscriLens在理解和减少算法歧视方面提供了信息丰富的指导。