What does it mean for a machine learning model to be `fair', in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise `fairness' in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.
翻译:机器学习模式“公平”是指什么?公平是指确保人人有获得某种好处的同等可能性,还是我们应力求最大限度地减少对最不利者的伤害?相关理想能否参照不存在某种特定社会歧视模式的另一种情况来确定?最近文献中提出的各种定义对歧视和公平等术语的含义以及如何用数学术语界定这些术语作出不同的假设。 歧视、平等主义和正义问题对于道德和政治哲学家来说具有重大意义,他们在将这些中心概念正规化和捍卫方面作出了重大努力;因此,在机器学习中正式化“公平”的尝试含有这些旧哲学辩论的反响是不足为奇的。本文件借鉴了道德和政治哲学的现有工作,以阐明关于公平机器学习的新辩论。