A clear, source-aware look at how bias shows up across the tech pipeline—from job posts and resume ranking to interviews, promotions, and AI hiring tools. Across three chapters, we follow audit notes, rubrics, and product change logs to separate allegation from proof and map small, repeatable fixes: tighter must-haves, anchored collaboration scores, blind rescans of low-scored resumes, sponsorship tied to real scope, and model cards with fairness checks. The series closes with a cadence any org can run monthly and quarterly, turning claims into a record. For listeners who want practical steps that raise signal, reduce noise, and keep the bar clear.