The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness
| Autor: | Watkins, Elizabeth Anne, McKenna, Michael, Chen, Jiahao |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
I.2
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Logic in Computer Science Computer Science - Artificial Intelligence 68T27 03B70 F.4 K.4 K.5 Machine Learning (cs.LG) Logic in Computer Science (cs.LO) Computer Science - Computers and Society Artificial Intelligence (cs.AI) Computers and Society (cs.CY) |
| DOI: | 10.48550/arxiv.2202.09519 |
| Popis: | 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. Comment: 10 pages, 1 figure, 2 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|