RISED is a structured pre-deployment safety framework that flags failures in clinical AI systems across reliability, inclusivity, sensitivity, equity, and deployability using pre-specified criteria and statistical corrections, even when aggregate accuracy looks strong.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Human-AI teams achieve complementarity only if error correlation ρ_HM < ρ* (with ρ* ≈ a near chance), impossibility above the threshold, gains scaling as Θ(√Δd), and multi-class threshold ρ*_K ≈ ρ*/√(K-1), matching image classification data at R=0.94.
citing papers explorer
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RISED: A Pre-Deployment Safety Evaluation Framework for Clinical AI Decision-Support Systems
RISED is a structured pre-deployment safety framework that flags failures in clinical AI systems across reliability, inclusivity, sensitivity, equity, and deployability using pre-specified criteria and statistical corrections, even when aggregate accuracy looks strong.
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When Can Human-AI Teams Outperform Individuals? Tight Bounds with Impossibility Guarantees
Human-AI teams achieve complementarity only if error correlation ρ_HM < ρ* (with ρ* ≈ a near chance), impossibility above the threshold, gains scaling as Θ(√Δd), and multi-class threshold ρ*_K ≈ ρ*/√(K-1), matching image classification data at R=0.94.