Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2507.11548.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.11548 v3

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

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This paper cites an unresolved cited work.

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

Reference 1

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening fast” and more advanced “thinking/slow

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This paper cites Name Redacted.

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

Reference 3

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This paper cites The candidate was applying for the Director of Finance position in PepsiCo’s Snacks Divi sion (see Supplemental Materials, Appendix A, Item 1).

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening The candidate was applying for the Director of Finance position in PepsiCo’s Snacks Divi sion (see Supplemental Materials, Appendix A, Item 1)

Reference 4

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This paper cites The candidate was applying for an HR Manager role at a Fortune 500 company (see Supplemental Materials, Appendix A, Item 2).

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening The candidate was applying for an HR Manager role at a Fortune 500 company (see Supplemental Materials, Appendix A, Item 2)

Reference 5

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

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This paper cites Chan and Walter J.

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

Reference 7

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

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

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This paper cites Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions

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

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This paper cites Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Reference 12

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This paper cites 2025 Job Seeker Nation Report.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening 2025 Job Seeker Nation Report

Reference 13

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

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

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

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Observation 6ec4d54c-8f7b-493c-8ef2-286e11e02652 · outbound

This paper cites Correll, Stephen Benard, and In Paik.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Correll, Stephen Benard, and In Paik

Reference 17

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Observation ce318e08-2af9-4af0-9f76-b52534a707c4 · outbound

This paper cites Insight - Amazon scraps secret AI recruiting tool that showed bias against women | Reuters.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Insight - Amazon scraps secret AI recruiting tool that showed bias against women | Reuters

Reference 18

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

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening These methods aim to correct for bias by re-weighting data, adding fairness constraints to algorithms, or adjusting model outputs

Reference 21

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This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Fuller, Manjari Raman, Eva Sage-Gavin, and Kristen Hines

Reference 24

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Auditing the Use of Language Models to Guide Hiring Decisions

Reference 26

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening The Myth in the Methodology: Towards a Recontextualization of Fairness in Machine Learning

Reference 28

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Bias in Large Language Models: Origin, Evaluation, and Mitigation

Reference 29

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

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

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening The Future of Recruiting 2025 | LinkedIn

Reference 34

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

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Half of Managers Use AI To Determine Who Gets Promoted and Fired

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

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening What's in a Name? Auditing Large Language Models for Race and Gender Bias

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Selbst, Danah Boyd, Sorelle A

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Finance” résumé (Highly Qualified) 16 Item 2: “HR

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

Reference 100

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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening Retrieved from https://proceedings.mlr.press/v81/buolamwini18a.html

Reference 2018

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Pith citing papers

No inbound Pith citation observations are available.