Biased AI Outputs Can Impact Humans’ Implicit Bias: A Case Study of the Impact of Gender-Biased Text-to-Image Generators
Year: 2025
Type: conference-paper
Abstract: A wave of recent work demonstrates that text-to-image generators (i.e., t2i) can perpetuate and amplify stereotypes about social groups. This research asks: what are the implications of biased t2i for humans who interact with these systems? Across three human-subjects studies, 1,881 participants engaged in a simulated t2i interaction in which the output was controlled to appear either stereotypic, gender-balanced, or counter-stereotypic, via the ratio of perceived women and men in the output of occupation prompts (e.g., a physicist). We then measured people's implicit gender bias using a gender-brilliance implicit association task (IAT), a bias that both relates to stereotypic occupation output in t2i and that has implications for women's representation in different fields. Participants who interacted with neutral t2i output (including only gender-neutral objects, e.g., DVDs) showed relatively high implicit gender-brilliance bias at baseline. Stereotypic t2i output did not increase implicit gender bias relative to this baseline (Study 1). However, participants exposed to counter-stereotypic t2i output had significantly lower implicit gender bias than participants exposed to only gender-neutral output (Studies 1 and 2). Although counter-stereotypic t2i may reduce implicit gender bias amongst users, less than 5% of participants actually preferred the counter-stereotypic representations of women and men. Instead, most participants preferred representations that accurately reflect gender distributions in society or that are more gender-balanced (Study 3). This work demonstrates a novel approach to studying human-AI interaction and reveals important insights for designing generative AI that seeks to mitigate harm. In particular, these findings have implications for understanding the impact of stereotypic t2i on human users, bias mitigation strategies via counter-stereotypic t2i output, and how these impacts (mis)align with people's preferences for t2i representations.
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
Awards: Collaborative Proposal: SaTC: Frontiers: Securing the Future of Computing for Marginalized and Vulnerable Populations, Collaborative Proposal: SaTC: Frontiers: Securing the Future of Computing for Marginalized and Vulnerable Populations, 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: W4415195281