Abstract
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 consequentialism, 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.
Highlights
In recent years, computer scientists have increasingly come to recognize that artificial intelligence (AI) systems have the potential to create harmful consequences
We review the dominant ideas about fairness in the machine learning literature, and provide the first critique of these ideas explicitly from the perspective of consequentialism
We will begin with an in-depth overview of consequentialism that engages with these difficulties, and show that it provides a useful critical perspective on conventional thinking about fairness within machine learning
Summary
Computer scientists have increasingly come to recognize that artificial intelligence (AI) systems have the potential to create harmful consequences. There are numerous difficulties with consequentialism in practice (see section 4), it provides a clear and principled foundation from which to critique proposals which fall short of its ideals. We analyze the literature on fairness within machine learning, and show how it largely depends on assumptions which the consequentialist perspective reveals immediately to be problematic. We provide an accessible overview of the main ideas of consequentialism (section 3), as well as a discussion of its difficulties (section 4), with a special emphasis on computational limitations. We review the dominant ideas about fairness in the machine learning literature (section 5), and provide the first critique of these ideas explicitly from the perspective of consequentialism (section 6). We conclude with a broader discussion of the ethical issues raised by learning and randomization, highlighting future direction for both AI and consequentialism (section 7)
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