On Consequentialism and Fairness

In recent years, computer scientists have increasingly com e to recognize that artificial intelligence (AI) systems have the potential to create harmful consequences. Especially within machine learning, there have been numerous efforts to formally characterize various not ions of fairness and develop algorithms to satisfy these criteria. However, most of this research has proceede d without any nuanced discussion of ethical foundations. Partly as a response, there have been several r ecent calls to think more broadly about the ethical implications of AI (Barabas et al., 2018; Hu and Chen, 2018b; Torresen, 2018; Green, 2019). Among the most prominent approaches to ethics within philos ophy is a highly influential position known as consequentialism. Roughly speaking, the consequentialist believes that out comes are all that matter, and that people should therefore endeavour to act so as to produce the best consequences, based on an impart ial perspective as to what is best . Although there are numerous difficulties with consequentia lism in practice (see §4), it nevertheless provides a clear and principled foundation from which to critiq ue proposals which fall short of its ideals. In this paper, we analyze the literature on fairness within mac hine learning, and show how it largely depends on assumptions which the consequentialist perspective rev eals immediately to be problematic. In particular, we make the following contributions: - We provide an accessible overview of the main ideas of conseq uentialism ( §3), as well as a discussion of its difficulties ( §4), with a special emphasis on computational limitations. 1 - We review the dominant ideas about fairness in the machine le arning literature ( §5), and provide the first critique of these ideas explicitly from the perspectiv e of consequentialism ( §6). - We conclude with a broader discussion of the ethical issues r aised by learning and randomization, highlighting future direction for both AI and consequentia lism ( §7).