The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness
This addresses a critical issue for researchers and practitioners in responsible AI by highlighting how oversimplified abstractions can lead to ethical harms in algorithmic fairness, though it is incremental in critiquing existing practices.
The paper tackles the problem of how the four-fifths rule from discrimination law has been abstracted into the disparate impact metric in AI fairness, incorrectly introducing new ethical harms and erasing legal nuances. It surveys the amplification of these harms in AI fairness toolkits, calling for computer scientists to re-evaluate their abstractions in AI ethics.
Computer scientists are trained to create 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 4/5 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 4/5 rule. We also survey how the field has amplified the potential for harm in codifying the 4/5 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.