To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised.

中文翻译：

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抵押贷款中的算法公平：从绝对条件到关系权衡

为了解决越来越多的人担心算法决策可能会加强歧视性偏见，研究人员提出了许多公平的概念和相应的数学形式化。这些概念中的每一个通常都表现为一种万能的绝对条件；然而，在现实中，实际和道德之间的权衡是不可避免的，而且更为复杂。我们引入了一种将公平性考虑在内的新方法——不是作为二元的、绝对的数学条件——而是作为与替代决策过程相比的关系概念。以美国抵押贷款为例，我们讨论了每个公平定义的道德基础，并证明我们提出的方法更接近于决策者的道德权衡，