[论文解读] The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Brent Mittelstadt, Sandra Wachter+1 more|arXiv (Cornell University)|Feb 5, 2023

Ethics and Social Impacts of AI被引用 16

一句话总结

本文认为许多公平机器学习实践会带来降格并主张通过最低伤害阈值和基于伤害的框架实现实质性平等。它批评当前的衡量与报告实践，并提出用于公平ML的设计变革。

ABSTRACT

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.

研究动机与目标

• 激发对当前公平ML定义常常降低某些群体福祉或机会的批评。
• 探讨哲学、法律与平等理论，以评估公平性作为ML中的分配原则。
• 识别现有公平ML做法如何促成降级和证据不足的正当性。
• 提出超越严格平等主义的面向设计的步骤，以实现公平ML中的实质性平等。

提出的方法

• 将平等与分配正义的哲学与法律理论应用于ML公平性的研究。
• 分析当前公平ML方法中降格现象的普遍性与原因。
• 批判性评估公平ML中的衡量、报告与分析做法。

实验结果

研究问题

• RQ1 当前的公平ML定义与衡量是否在执行公平约束时证明了降格？
• RQ2 如何重新设计ML公平以避免降格并促进实质性平等？
• RQ3 哪些框架，如最低伤害阈值，可以把基于伤害的公平性方法落到实处？
• RQ4 在ML中默认严格平等主义的法律与哲学批评有哪些？

主要发现

• FairML 往往在整体性能的代价下实现公平性，或导致对某些群体的降格。
• 当前在公平ML中的衡量与报告做法不足以在实践中为降格提供正当性。
• 通过执行最低可接受伤害阈值，即最低率约束，作为公平约束，可以推进实质性平等的方法。
• 一种替代性的基于伤害的框架可能将焦点从严格的平等主义转向实质性平等机会。