Fairness in Machine Learning: Lessons from Political Philosophy
Fairness in Machine Learning: Lessons from Political Philosophy
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机器学习的公平性：政治哲学的教训
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Reuben Binns
中科院分区：
文献类型：
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作者：
Reuben Binns
从可操作的角度来看，机器学习模型的“公平”意味着什么？公平应该包括确保每个人都有平等的机会获得某种利益，还是我们应该以最小化对最弱势群体的伤害为目标？相关的理想是否可以通过参考某些不存在特定社会歧视模式的其他事态来确定？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定义它们做出了不同的假设。歧视、平等主义和正义问题是道德和政治哲学家非常感兴趣的问题，他们在形式化和捍卫这些核心概念方面付出了巨大的努力。因此，在机器学习中形式化“公平”的尝试包含了这些古老的哲学辩论的回声，这并不奇怪。本文借鉴了道德和政治哲学方面的现有工作，以阐明关于公平机器学习的新兴辩论。
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.