因果路径与子群体差异分析的实用指南
Practical Guide for Causal Pathways and Sub-group Disparity Analysis
AI总结：
本文提出将因果分解分析与异质性评估相结合，用于揭示敏感属性对结果的因果路径和子群体差异，并通过两个种族相关数据集验证该方法能识别受差异影响最大且机器学习分类误差最大的子群体。
摘要：
在本研究中，我们引入因果差异分析（causal disparity analysis）的应用，以揭示真实世界观测数据中敏感属性与目标结果之间错综复杂的关系和因果路径。我们的方法采用因果分解分析（causal decomposition analysis）来量化和检验敏感属性与结果之间的因果交互作用。我们还强调在因果差异分析中整合异质性评估（heterogeneity assessment）的重要性，以便更深入地理解特定子群体中敏感属性对结果的影响。我们的两步调查聚焦于以种族作为敏感属性的数据集。在两个数据集上的结果表明，利用因果分析和异质性评估不仅有利于量化数据中的偏差，而且有利于厘清这些偏差对结果的影响。我们证明，通过我们的方法识别出的受差异影响最大的子群体，正是机器学习分类误差最大的子群体。我们还表明，仅基于敏感属性对数据进行分组是不够的，通过这些分析，我们可以找到直接受差异影响的子群体。我们希望我们的发现能够鼓励在未来的伦理人工智能实践和偏见审计中采用此类方法，从而营造一个更加公平和公正的技术环境。
英文摘要：
In this study, we introduce the application of causal disparity analysis to unveil intricate relationships and causal pathways between sensitive attributes and the targeted outcomes within real-world observational data. Our methodology involves employing causal decomposition analysis to quantify and examine the causal interplay between sensitive attributes and outcomes. We also emphasize the significance of integrating heterogeneity assessment in causal disparity analysis to gain deeper insights into the impact of sensitive attributes within specific sub-groups on outcomes. Our two-step investigation focuses on datasets where race serves as the sensitive attribute. The results on two datasets indicate the benefit of leveraging causal analysis and heterogeneity assessment not only for quantifying biases in the data but also for disentangling their influences on outcomes. We demonstrate that the sub-groups identified by our approach to be affected the most by disparities are the ones with the largest ML classification errors. We also show that grouping the data only based on a sensitive attribute is not enough, and through these analyses, we can find sub-groups that are directly affected by disparities. We hope that our findings will encourage the adoption of such methodologies in future ethical AI practices and bias audits, fostering a more equitable and fair technological landscape.
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。
