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No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy

Year: 2025
Type: conference-paper
Abstract: Despite bias in artificial intelligence (AI) being a risk of their use in hiring systems, there is no large-scale empirical investigation of the impacts of these biases on hiring decisions made collaboratively between people and AI systems. It is also unknown whether AI literacy, people's own biases, and behavioral interventions intended to reduce discrimination affect these human-in-the-loop AI teaming (AI-HITL) outcomes. In this study, we conduct a resume-screening experiment (N=528) where people collaborate with simulated AI models exhibiting race-based preferences (bias) to evaluate candidates for 16 high and low status occupations. Simulated AI bias approximates factual and counterfactual estimates of racial bias in real-world AI systems. We investigate people's preferences for White, Black, Hispanic, and Asian candidates (represented through names and affinity groups on quality-controlled resumes) across 1,526 scenarios and measure their unconscious associations between race and status using implicit association tests (IATs), which predict discriminatory hiring decisions but have not been investigated in human-AI collaboration. This evaluation framework can generalize to other groups, models, and domains. When making decisions without AI or with AI that exhibits no race-based preferences, people select all candidates at equal rates. However, when interacting with AI favoring a particular group, people also favor those candidates up to 90\% of the time, indicating a significant behavioral shift. The likelihood of selecting candidates whose identities do not align with common race-status stereotypes can increase by 13\% if people complete an IAT before conducting resume screening. Finally, even if people think AI recommendations are low quality or not important, their decisions are still vulnerable to AI bias under certain circumstances. This work has implications for people's autonomy in AI-HITL scenarios, AI and work, design and evaluation of AI hiring systems, and strategies for mitigating bias in collaborative decision-making tasks. In particular, organizational and regulatory policy should acknowledge the complex nature of AI-HITL decision making when implementing these systems, educating people who use them, and determining which are subject to oversight.
DOI: 10.1609/aies.v8i3.36749
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: W4415230829
FWCI: 6.345
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Cited by: 6
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