Integrating complementary fairness metrics into AutoML pipeline optimization yields 14.5% better average fairness, 35.7% less data usage, and simpler models, with a 9.4% drop in predictive performance versus a performance-only baseline.
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Exploring the impact of fairness-aware criteria in AutoML
Integrating complementary fairness metrics into AutoML pipeline optimization yields 14.5% better average fairness, 35.7% less data usage, and simpler models, with a 9.4% drop in predictive performance versus a performance-only baseline.