Racial and Gender Disparities in AI-Based Resume Screening: An Audit Study of 12 Corporate Automated Hiring Platforms

This study investigates racial and gender disparities in callback rates produced by AI-based resume screening systems across 12 corporate automated recruitment platforms within the context of AI ethics and computational social science, an area of growing scientific importance given its implications for AI hiring system auditing requirements, equal employment opportunity enforcement, and fair ML model development standards. Using correspondence audit study submitting 4,800 resumes (400 per platform) with factorial manipulation of applicant name racial/gender signaling while holding all qualifications constant, we examine AI screening model training data encoding historical hiring biases translating into differential callback rates for racially/gender-coded names even with equivalent qualifications in 4,800 resume submissions to 12 AI hiring platforms across 8 industry sectors with 4 name conditions (Black female, Black male, White female, White male) drawn from online resume submission portals of 12 large U.S. corporations using AI-based applicant tracking systems for initial resume screening. Results indicate that AI screening systems produce mean callback rates of 34.2% for White male names versus 21.4% for Black female names (12.8 pp gap), with largest disparities in finance (18.4 pp) and technology (16.2 pp) sectors (p < 0.001), with 12.8 pp gap (34.2% White male vs. 21.4% Black female callback rate) as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to AI ethics and computational social science and carry actionable implications for the design of programs and policies targeting AI hiring system auditing requirements, equal employment opportunity enforcement, and fair ML model development standards.