超越不兼容性：机器学习与法律中互斥公平性标准之间的权衡
Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Law
AI总结：
本文提出FAIM算法，在互斥的算法公平性标准间进行连续插值，使预测能部分满足加权公平条件，并探讨其在传统法律与新AI法规中的合规潜力。
摘要：
公平且可信赖的AI在机器学习和法律领域都变得越来越重要。一个重要的结果是，决策者必须努力保证一种“公平的”，即非歧视性的算法决策程序。然而，存在几种相互竞争的算法公平性概念，这些概念在现实的事实假设下已被证明是互不兼容的。例如，这涉及广泛使用的“组内校准”和“正/负类平衡”公平性度量。在本文中，我们提出了一种新颖的算法（FAir Interpolation Method: FAIM），用于在这三个公平性标准之间进行连续插值。因此，最初不公平的预测可以被纠正，以至少部分地满足各自公平性条件的期望加权组合。我们展示了我们的算法在应用于合成数据、COMPAS数据集以及来自电子商务领域的一个新的真实世界数据集时的有效性。最后，我们讨论了FAIM在多大程度上可以被利用来遵守相互冲突的法律义务。分析表明，它可能将传统法律领域（如信用评分和刑事诉讼）中的义务操作化，但也适用于欧盟提出的最新AI法规，如《数字市场法》和最近颁布的《AI法》。
英文摘要：
Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a 'fair', i.e., non-discriminatory, algorithmic decision procedure. However, there are several competing notions of algorithmic fairness that have been shown to be mutually incompatible under realistic factual assumptions. This concerns, for example, the widely used fairness measures of 'calibration within groups' and 'balance for the positive/negative class'. In this paper, we present a novel algorithm (FAir Interpolation Method: FAIM) for continuously interpolating between these three fairness criteria. Thus, an initially unfair prediction can be remedied to, at least partially, meet a desired, weighted combination of the respective fairness conditions. We demonstrate the effectiveness of our algorithm when applied to synthetic data, the COMPAS data set, and a new, real-world data set from the e-commerce sector. Finally, we discuss to what extent FAIM can be harnessed to comply with conflicting legal obligations. The analysis suggests that it may operationalize duties in traditional legal fields, such as credit scoring and criminal justice proceedings, but also for the latest AI regulations put forth in the EU, like the Digital Markets Act and the recently enacted AI Act.
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。
