Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening

TL;DR

This paper critically examines AI resume screening tools, revealing that some lack genuine evaluative competence and exhibit intersectional biases, emphasizing the need for dual validation of fairness and capability.

Contribution

It introduces the 'Illusion of Neutrality' concept and demonstrates the importance of auditing AI systems for both bias and true competence in hiring.

Findings

Some models penalize demographic signals, indicating bias.

Certain models lack core evaluative competence, relying on superficial cues.

Dual-validation is recommended for fair and effective AI hiring tools.

Abstract

The increasing use of generative AI for resume screening is predicated on the assumption that it offers an unbiased alternative to biased human decision-making. However, this belief fails to address a critical question: are these AI systems fundamentally competent at the evaluative tasks they are meant to perform? This study investigates the question of competence through a two-part audit of eight major AI platforms. Experiment 1 confirmed complex, contextual racial and gender biases, with some models penalizing candidates merely for the presence of demographic signals. Experiment 2, which evaluated core competence, provided a critical insight: some models that appeared unbiased were, in fact, incapable of performing a substantive evaluation, relying instead on superficial keyword matching. This paper introduces the "Illusion of Neutrality" to describe this phenomenon, where an apparent lack of bias is merely a symptom of a model's inability to make meaningful judgments. This study recommends that organizations and regulators adopt a dual-validation framework, auditing AI hiring tools for both demographic bias and demonstrable competence to ensure they are both equitable and effective.