The four-fifths rule is not disparate impact: A woeful tale of epistemic trespassing in algorithmic fairness
Pages 764 - 775
Abstract
Computer scientists are trained in the art of creating abstractions that simplify and generalize. However, a premature abstraction that omits crucial contextual details creates the risk of epistemic trespassing, by falsely asserting its relevance into other contexts. We study how the field of responsible AI has created an imperfect synecdoche by abstracting the four-fifths rule (a.k.a. the <Formula format="inline"><TexMath><?TeX $\nicefrac {4}{5}$?></TexMath><AltText>Math 1</AltText><File name="facct24-53-inline1" type="svg"/></Formula> rule or 80% rule), a single part of disparate impact discrimination law, into the disparate impact metric. This metric incorrectly introduces a new deontic nuance and new potentials for ethical harms that were absent in the original <Formula format="inline"><TexMath><?TeX $\nicefrac {4}{5}$?></TexMath><AltText>Math 2</AltText><File name="facct24-53-inline2" type="svg"/></Formula> rule. We also survey how the field has amplified the potential for harm in codifying the <Formula format="inline"><TexMath><?TeX $\nicefrac {4}{5}$?></TexMath><AltText>Math 3</AltText><File name="facct24-53-inline3" type="svg"/></Formula> rule into popular AI fairness software toolkits. The harmful erasure of legal nuances is a wake-up call for computer scientists to self-critically re-evaluate the abstractions they create and use, particularly in the interdisciplinary field of AI ethics.
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- The four-fifths rule is not disparate impact: A woeful tale of epistemic trespassing in algorithmic fairness
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