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.

翻译：近年来，机器学习（ML）中的公平性已成为一个高度活跃的研究与开发领域。大多数研究以简单术语定义公平，即通过减少不同人口群体间性能或结果的差距，同时尽可能保留原始系统的准确性。这种通过公平度量对平等概念的过度简化令人担忧。当前许多公平度量同时存在公平性与性能的退化问题，即"拉平效应"——通过使所有群体状况恶化，或将表现较好的群体拉低至最差群体水平来实现公平。当公平只能通过使每个人在物质或关系层面变得更糟（如污名伤害、丧失团结、不平等关切、错失实质性平等机会）来实现时，将模糊的"公平"概念转化为实践的过程中显然出现了问题。本文考察了公平机器学习中拉平效应的成因与普遍性，并基于平等与分配正义的哲学、法律理论以及平等法律判例学，探讨了可能的辩护理由与批判观点。我们发现，当前公平机器学习实践尚未采用必要的测量、报告或分析方法来论证拉平效应的合理性。我们提出迈向公平机器学习实质性平等的第一步：通过设定最低可接受伤害阈值（即"最低比率约束"）作为公平约束，从设计层面实现"拉平提升"系统。同时，我们提出一个替代性伤害框架，以对抗当前该领域主导的过度简化的平等主义框架，推动未来讨论更多关注实质性平等机会，而非默认采用严格平等主义。（注：此为摘要删节版，完整摘要请参见原文。）