Optimal thresholds under capacity limits and noisy compliance outperform accuracy-maximizing policies; OpAUC aligns algorithm choice with operational outcomes.
Forτ < τ ∗A score, we have ˜W ′(τ) =M ˜R′(τ)>0, and for τ ∗A score < τ < τ c, we have ˜W ′(τ) =M ˜R′(τ)<0
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Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance
Optimal thresholds under capacity limits and noisy compliance outperform accuracy-maximizing policies; OpAUC aligns algorithm choice with operational outcomes.