Get started
Work

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

Year: 2024
Type: conference-paper
Abstract: Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the same. However, given the biases which are embedded within LLMs, it is unclear whether they can be used in this scenario without disadvantaging groups based on their protected attributes. In this work, we investigate the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection. Using that framework, we then perform a resume audit study to determine whether a selection of Massive Text Embedding (MTE) models are biased in resume screening scenarios. We simulate this for nine occupations, using a collection of over 500 publicly available resumes and 500 job descriptions. We find that the MTEs are biased, significantly favoring White-associated names in 85.1% of cases and female-associated names in only 11.1% of cases, with a minority of cases showing no statistically significant differences. Further analyses show that Black males are disadvantaged in up to 100% of cases, replicating real-world patterns of bias in employment settings, and validate three hypotheses of intersectionality. We also find an impact of document length as well as the corpus frequency of names in the selection of resumes. These findings have implications for widely used AI tools that are automating employment, fairness, and tech policy.
DOI: 10.1609/aies.v7i1.31748
Language: English
Subfield: Safety Research
Open Access status: bronze
Award: PURPOSE: ARTIFICIAL INTELLIGENCE (AI) IS WIDESPREAD IN TODAY'S SOCIETY, WITH AI ALGORITHMS BEING INCREASINGLY RELIED ON TO MAKE CONSEQUENTIAL DECISIONS ABOUT INDIVIDUALS. ALTHOUGH THERE WAS INITIAL OPTIMISM THAT AI APPLICATIONS WOULD MAKE A MORE EFFICIENT AND FAIR SOCIETY, ANECDOTAL EXAMPLES OF BIASED OUTCOMES IN AI APPLICATIONS NOW ABOUND FROM DOMAINS AS WIDE AS WEB SEARCH, MACHINE TRANSLATION, UNIVERSITY ADMISSIONS, RESUME SCREENING, AND JOB CANDIDATE ASSESSMENT. YET, BEYOND THESE ANECDOTES, THERE REMAINS NO COMPREHENSIVE STUDY THAT EXAMINES (1) HOW THE EXTENT OF BIASES IN GENERAL-PURPOSE MODALITY REPRESENTATIONS THAT AI RELIES ON PROPAGATE TO DOWNSTREAM DECISIONS, (2) HOW BIASED AI DECISIONS INTERACT WITH THE BIASES OF HUMANS-IN-THE-LOOP, AND (3) HOW AI-MEDIATED OUTCOMES IMPACT THE BIASES OF FUTURE GENERATIONS OF AI MODELS. THE PROPOSED RESEARCH SEEKS TO ADDRESS THESE NEEDS, THEREBY CONTRIBUTING TO NIST'S TRUSTWORTHY AND RESPONSIBLE AI EFFORTS. ACTIVITIES TO BE PERFORMED: IN THIS PROJECT, THE PROPOSERS WILL ANALYZE THE IMPACT OF BIASED GENERAL-PURPOSE MACHINE REPRESENTATIONS GENERATED VIA UNSUPERVISED MACHINE LEARNING ON THE AUTOMATED DECISIONS OF SUPERVISED MACHINE LEARNING ALGORITHMS THAT RELY ON STRUCTURED DATA IN THE EMPLOYMENT DOMAIN. AS A STARTING POINT, THE PROPOSERS WILL FOCUS ON GENDER AND RACIAL BIAS IN RESUME SCREENING AND JOB CANDIDATE ASSESSMENT, AN APPLICATION DOMAIN WITH CLEAR CONSEQUENCES FOR LIFE'S OPPORTUNITIES AND OUTCOMES. THE PROPOSERS WILL DEVELOP A BIAS PROPAGATION AND INTERACTION AUDIT FRAMEWORK THAT GENERALIZES ACROSS MODALITIES, BIAS TYPES, AND APPLICATION DOMAINS. EXPECTED OUTCOMES: THE PROPOSED RESEARCH WILL CONDUCT TWO COMPLEMENTARY STUDIES TO ANSWER THE FOLLOWING IMPORTANT QUESTIONS: (1) GIVEN THAT BIAS REFLECTED FROM SOCIETY TO LARGE-SCALE DATASETS GETS EMBEDDED IN GENERAL-PURPOSE MACHINE REPRESENTATIONS USED FOR PROCESSING DATA, HOW DOES REPRESENTATIONAL BIAS PROPAGATE TO DOWNSTREAM APPLICATIONS? (2) HOW ARE HUMANS-IN-THE-LOOP COLLABORATING WITH AI IN DECISION MAKING AFFECTED BY THE BIASED OUTCOMES OF MACHINES,GIVEN THEIR OWN IMPLICIT BIASES? THE RESEARCH STUDIES SUPPORTED BY THIS GRANT WILL BE PUBLISHED IN THE SCIENTIFIC LITERATURE, SPECIFICALLY BY CONTRIBUTING PAPERS ON AN ANNUAL BASIS AT ARTIFICIAL INTELLIGENCE AND FAIRNESS VENUES FOR COMPUTER AND INFORMATION SCIENCES. INTENDED BENEFICIARIES: SOCIOTECHNICAL ARTIFICIAL INTELLIGENCE (AI) SYSTEMS WITH HUMANS-IN-THE-LOOP MAKE CONSEQUENTIAL DECISIONS ABOUT INDIVIDUALS IN SCENARIOS THAT DETERMINE LIFE'S OPPORTUNITIES AND OUTCOMES. DEVELOPING METHODS AND METRICS TO MEASURE HOW THE BIASES OF SOCIETY, AI, AND HUMANS-IN-THE-LOOP INTERACT IN DECISION MAKING WILL PROVIDE QUANTITATIVE EVIDENCE ON THE IMPACT OF AI ON SOCIETY. THIS PROJECT WILL BENEFIT THE GENERAL PUBLIC BY PROVIDING MUCH-NEEDED RESEARCH TO SUPPORT DEVELOPING STANDARDS FOR IDENTIFYING AND MANAGING BIAS IN AI. FAIR WITH HARMFUL BIAS MANAGED IS A KEY CHARACTERISTIC OF TRUSTWORTHY AI AS DEFINED IN NIST'S AI RISK MANAGEMENT FRAMEWORK, THEREFORE THIS BIAS RESEARCH WILL CLEARLY BENEFIT NIST'S LARGER TRUSTWORTHY AND RESPONSIBLE AI PROGRAM AND ADVANCE THE STATE-OF-THE-ART FOR BIAS EVALUATIONS IN AI SYSTEMS. SUBRECIPIENT ACTIVITIES: THERE ARE NO PLANNED SUBAWARDS.
OpenAlex ID: W4404518169
FWCI: 34.22
Cites:
Cited by: 73
Related to: 10