公平机器学习的不公平性：默认的水平化和严格的平均主义
The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
摘要：
近年来，机器学习（ML）中的公平性已经成为研究和开发的一个高度活跃的领域。大多数人对公平的定义很简单，公平意味着减少人口群体之间在业绩或成果方面的差距，同时尽可能保持原始系统的准确性。这种通过公平措施实现平等的过度简单化令人不安。许多当前的公平措施都遭受公平和性能退化，或“拉平”，其中公平是通过使每个群体的情况更糟，或通过将表现较好的群体降低到最差水平来实现的。当公平只能通过伤害耻辱、失去团结、不平等的关注和错过实质性平等的机会，使每个人在物质或关系方面变得更糟来实现时，在将模糊的“公平”概念转化为实践方面似乎出了问题。本文探讨了在fairML中降低水平的原因和普遍性，并基于平等和分配正义的哲学和法律理论以及平等法法理学探讨了可能的理由和批评。我们发现，fairML目前没有进行必要的测量、报告或分析，以证明在实践中降低水平的合理性。我们提出了在fairML中实现实质性平等的第一步：通过执行最低可接受伤害阈值或“最低速率约束”作为公平约束，通过设计来“升级”系统。我们同样提出了一个替代的基于伤害的框架，以对抗目前在该领域占主导地位的过于简化的平等主义框架，并推动未来的讨论更多地走向实质性的平等机会，远离严格的平等主义默认。N.B.摘要缩短，完整摘要见论文。
英文摘要：
In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. This oversimplification of equality through fairness measures is troubling. Many current fairness measures suffer from both fairness and performance degradation, or "levelling down," where fairness is achieved by making every group worse off, or by bringing better performing groups down to the level of the worst off. When fairness can only be achieved by making everyone worse off in material or relational terms through injuries of stigma, loss of solidarity, unequal concern, and missed opportunities for substantive equality, something would appear to have gone wrong in translating the vague concept of 'fairness' into practice. This paper examines the causes and prevalence of levelling down across fairML, and explore possible justifications and criticisms based on philosophical and legal theories of equality and distributive justice, as well as equality law jurisprudence. We find that fairML does not currently engage in the type of measurement, reporting, or analysis necessary to justify levelling down in practice. We propose a first step towards substantive equality in fairML: "levelling up" systems by design through enforcement of minimum acceptable harm thresholds, or "minimum rate constraints," as fairness constraints. We likewise propose an alternative harms-based framework to counter the oversimplified egalitarian framing currently dominant in the field and push future discussion more towards substantive equality opportunities and away from strict egalitarianism by default. N.B. Shortened abstract, see paper for full abstract.
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。
