机器学习中判别的视觉分析
Visual Analysis of Discrimination in Machine Learning
摘要：
在犯罪预测和大学录取等关键应用中越来越多地使用自动化决策，这引发了人们对机器学习公平性的质疑。我们如何决定不同的待遇是合理的还是歧视性的？在本文中，我们调查歧视机器学习从视觉分析的角度来看，并提出了一个交互式可视化工具，DiscriLens，以支持更全面的分析。为了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确定了一组潜在歧视性项集。通过将扩展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，以促进歧视性项集的探索和解释。一项用户研究表明，用户可以快速准确地解释DiscriLens中的视觉编码信息。用例表明，DiscriLens在理解和减少算法歧视方面提供了信息指导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
