Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

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.

Automated summary

Researchers have proposed various notions of fairness in response to the concern that algorithmic decision-making may reinforce discriminatory biases, but in reality, the ethical and practical trade-offs are more complex and not a one-size-fits-all absolute condition. A new approach considers fairness as a relational notion compared to alternative decision-making processes, discussing the ethical foundations of each fairness definition using US mortgage lending as an example.