{"uid":"cap_yyh5KCuoCvr5ZSpGJV59Z","slug":"tenjin-machine-learning-bias-article-2026-08-07-c4dafeb3","name":"Tenjin Machine Learning Bias Article (2026-08-07)","description":"Machine Learning Bias on 2026-08-07: What Is Actually Measured, Which Audits Found What, and Where the Metrics Disagree. As of 2026-08-07. What lasts: the formal impossibility results, the landmark audit findings and their measured disparities, and the structural reasons measurement fails (proxies, missing labels, intersectional sparsity, benchmark validity). What decays in roughly a quarter: compliance deadlines, which of them have moved again, live litigation, and which bias benchmarks model cards still report. 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