A new statistical framework has been proposed to test for discriminant validity in predictive algorithms, aiming to identify when models predict unintended outcomes. This framework, drawing from causal inference and econometrics, compares calibrated prediction losses to assess if an algorithm performs better on intended outcomes than impermissible ones. The method was illustrated in an admissions setting, where it confirmed discriminant validity concerning gender but not race, and also analyzed in a criminal justice context, highlighting the need for complementary validity checks. AI
IMPACT Provides a statistical method to improve the reliability and safety of predictive algorithms by detecting unintended predictions.
RANK_REASON Academic paper proposing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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