Applying CMA-MAP-Elites to neural network weights produces a two-dimensional map of accuracy versus positive-prediction ratios, enabling selection of high-accuracy models that stay within an 80% rule fairness zone.
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Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management
Applying CMA-MAP-Elites to neural network weights produces a two-dimensional map of accuracy versus positive-prediction ratios, enabling selection of high-accuracy models that stay within an 80% rule fairness zone.