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:在诸如犯罪预测和大学录取等关键应用中越来越多地使用自动决策,引起了关于机器学习的公平性的问题。我们如何决定不同待遇是合理还是歧视性的?在本文件中,我们从视觉分析角度调查机器学习中的歧视,并提议一个互动可视化工具DiscriLens,以支持更全面的分析。为了披露关于算法歧视的详细信息,DiscriLens查明了一套基于因果建模和分类规则采矿的潜在歧视性物品。通过将扩大的Euler图与基于矩阵的可视化相结合,我们开发了一套新的可视化图集,以便利对歧视性物品的探索和解释。用户研究表明,用户可以快速准确地解读DiscriLens的视觉编码信息。使用案例表明DiscriLens在理解和减少算法歧视方面提供了信息指导。