A resume-scoring system built on BERT or RoBERTa reproduces artificially injected gender bias in training scores, and two gender-information removal methods reduce that bias on a synthetic recruitment dataset.
The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness
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abstract
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.
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cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment
A resume-scoring system built on BERT or RoBERTa reproduces artificially injected gender bias in training scores, and two gender-information removal methods reduce that bias on a synthetic recruitment dataset.