Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs

Minds and Machines 31 (1):165-191 (2021)
Download Edit this record How to cite View on PhilPapers
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.
No keywords specified (fix it)
PhilPapers/Archive ID
Upload history
Archival date: 2021-06-08
View other versions
Added to PP index

Total views
28 ( #57,093 of 2,432,203 )

Recent downloads (6 months)
16 ( #39,086 of 2,432,203 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.