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：摘要：近年来，机器学习中的公平性已成为一个高度活跃的研究与发展领域。多数研究以简单方式定义公平，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退化，即"降级平等"——通过使所有群体状况恶化，或将表现较好群体拉低至最差群体水平来实现公平。当公平只能通过使所有人在物质或关系层面遭受污名伤害、团结丧失、不平等关切及错失实质性平等机会而变得更糟时，将模糊的"公平"概念转化为实践显然出现了问题。本文考察公平机器学习中降级平等的成因与普遍性，基于平等与分配正义的哲学与法律理论，以及平等法判例，探讨其可能的正当性依据与批判观点。研究发现，当前公平机器学习并未开展实践层面证成降级平等所需的测量、报告或分析类型。我们提出迈向公平机器学习中实质性平等的第一步：通过强制执行最低可接受危害阈值（即"最低比率约束"）作为公平约束，实现系统设计层面的"升级平等"。同时提出替代性危害框架，以对抗当前领域盛行的过度简化的平等主义框架，推动未来讨论更多聚焦于实质性平等机会，而非默认的严格平等主义。注：此为精简版摘要，完整摘要请参阅原文。