This episode explores how AI hiring tools can inherit and amplify gender bias from historical training data. Lucas and Luna dig into a 2024 study by the EU's Fundamental Rights Agency that found an AI recruiting system penalized resumes with female-coded language from women's colleges, reducing callback rates by 12 percent compared to male-coded equivalents. They discuss why gender-blind algorithms don't fix the problem, the limits of 'fairness through unawareness,' and what companies like Amazon learned after scrapping their own biased AI recruiter in 2018. The conversation also touches on current regulatory efforts, including New York City's Local Law 144, which requires bias audits of hiring algorithms, and the practical steps firms can take to audit and retrain models. No hot takes—just a clear-eyed look at one of the most consequential ethical challenges in workplace AI.

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