On criterion weighting to reduce adverse impact

In research on adverse impact reduction (i.e., improving racial equality in hiring rates), one approach that is routinely mentioned in classic reviews is criterion weighting, or assigning more relative weight to contextual performance in the criterion measure. We revisit this recommendation, using an updated meta-analytic correlation matrix among predictors and criteria. Results show that, contrary to past beliefs, assigning more weight to contextual performance is typically unlikely to improve diversity and is also likely to harm criterion validity. Criterion weighting is also often outperformed by Pareto-optimal predictor weighting. Further, Pareto-optimal solutions show that, when holding criterion validity constant, assigning more criterion weight to contextual performance is likely to exacerbate rather than reduce adverse impact. We additionally offer a Global Pareto Frontier/outer envelope approach that simultaneously optimizes predictor weights and criterion weights to achieve the best adverse impact-criterion validity outcome pairs for a personnel selection system, and we identify circumstances needed for the joint optimization of predictor and criterion weights to outperform Pareto-optimal predictor weighting alone. (PsycInfo Database Record (c) 2026 APA, all rights reserved).