When Your AI Recruiter Rejects Based on Your Commute

Episode 37 of AI Ethics with Fexingo explores a hidden bias in hiring algorithms: using estimated commute distance as a proxy for job stability or socioeconomic status. Lucas and Luna dissect a 2025 Stanford study that found AI recruitment tools penalized applicants from lower-income neighborhoods by flagging longer commutes as a risk factor—even when those candidates were more qualified. They discuss the legal implications under the EEOC's 2024 guidance on algorithmic fairness, the difficulty of auditing black-box models for proxy discrimination, and why removing commute data alone isn't a fix. The hosts also touch on similar proxy issues in lending and insurance AI, and what engineers and policymakers can do to prevent fairness-washing. #AIEthics #HiringBias #AlgorithmicFairness #ProxyDiscrimination #RecruitmentAI #StanfordStudy #EEOC #ResponsibleAI #CommuteBias #SocioeconomicBias #BlackBoxAuditing #FairnessWashing #Technology #Business #FexingoBusiness #BusinessPodcast #LucasAndLuna #AIAccountability Keep every episode free: buymeacoffee.com/fexingo