论文标题
机器学习中的公平和随机性：统计独立性和相对化
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization
论文作者
论文摘要
公平的机器学习努力，以防止在社会中嵌入的机器学习应用程序中引起不公平。尽管公平性和拟议的“公平算法”的定义多种多样，但关于公平性仍然没有解决的概念问题。在本文中，我们剖析了统计独立性在机器学习中经常使用的公平和随机性概念中的作用。因此，我们得到了一个令人惊讶的假设：随机性和公平性可以被视为机器学习中的等效概念。 特别是，我们通过吸引冯·米塞斯（Von Mises）的百年基础来获得相对的随机性概念，以统计独立性表示为统计独立性。从与常用的I.I.D.随机性的抽象意义上说，这一概念是“正交的”。然后，使用机器学习中的标准公平概念，这些概念是通过统计独立性定义的，然后我们将有关数据的前随机性假设链接到公平预测的事后要求。这种联系证明了富有成果：我们用它来争辩说，随机性和公平本质上是相对的，并且这两个概念都应反映其本质作为机器学习中的建模假设。
Fair Machine Learning endeavors to prevent unfairness arising in the context of machine learning applications embedded in society. Despite the variety of definitions of fairness and proposed "fair algorithms", there remain unresolved conceptual problems regarding fairness. In this paper, we dissect the role of statistical independence in fairness and randomness notions regularly used in machine learning. Thereby, we are led to a suprising hypothesis: randomness and fairness can be considered equivalent concepts in machine learning. In particular, we obtain a relativized notion of randomness expressed as statistical independence by appealing to Von Mises' century-old foundations for probability. This notion turns out to be "orthogonal" in an abstract sense to the commonly used i.i.d.-randomness. Using standard fairness notions in machine learning, which are defined via statistical independence, we then link the ex ante randomness assumptions about the data to the ex post requirements for fair predictions. This connection proves fruitful: we use it to argue that randomness and fairness are essentially relative and that both concepts should reflect their nature as modeling assumptions in machine learning.
