Card Dallas, Smith Noah A
Computer Science Department, Stanford University, Stanford, CA, United States.
Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, United States.
Front Artif Intell. 2020 May 8;3:34. doi: 10.3389/frai.2020.00034. eCollection 2020.
Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is , the position that, roughly speaking, outcomes are all that matter. Although consequentialism is not free from difficulties, and although it does not necessarily provide a tractable way of choosing actions (because of the combined problems of uncertainty, subjectivity, and aggregation), it nevertheless provides a powerful foundation from which to critique the existing literature on machine learning fairness. Moreover, it brings to the fore some of the tradeoffs involved, including the problem of who counts, the pros and cons of using a policy, and the relative value of the distant future. In this paper we provide a consequentialist critique of common definitions of fairness within machine learning, as well as a machine learning perspective on consequentialism. We conclude with a broader discussion of the issues of learning and randomization, which have important implications for the ethics of automated decision making systems.
近期机器学习领域关于公平性的研究主要集中在如何定义、量化和促进“公平”结果。然而，对于这些努力背后的伦理基础关注较少。在应考虑的伦理视角中，有一种观点大致认为结果是唯一重要的，即结果主义。尽管结果主义并非没有困难，且不一定能提供一种可行的行动选择方式（由于不确定性、主观性和聚合性等综合问题），但它仍然为批判现有的机器学习公平性文献提供了一个有力的基础。此外，它还凸显了一些涉及的权衡，包括谁被纳入考量的问题、使用一种策略的利弊以及遥远未来的相对价值。在本文中，我们从结果主义的角度对机器学习中公平性的常见定义进行批判，并从机器学习的角度审视结果主义。我们最后对学习和随机化问题进行更广泛的讨论，这些问题对自动化决策系统的伦理有重要影响。
