Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912487662682112 |
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| author | Webster, Kevin T |
| author_facet | Webster, Kevin T |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11548 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Webster, Kevin T Computers and Society Artificial Intelligence Computation and Language I.2.1; K.4.2; I.2.6; K.4.1 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. |
| title | Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening |
| topic | Computers and Society Artificial Intelligence Computation and Language I.2.1; K.4.2; I.2.6; K.4.1 |
| url | https://arxiv.org/abs/2507.11548 |