Tag: explainable AI
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Measuring Rationale Stability for Explainable Pattern Recognition
Discover a novel metric to quantify rationale stability in explainable AI, enhancing consistency in pattern recognition under controlled perturbations.
Explainable Model Routing for Efficient AI Workflows
Discover Topaz, an explainable model routing framework that balances cost, performance, and transparency in AI agentic workflows for better results.
Feature Attribution Stability Suite: Evaluating Post-Hoc Methods
Discover how the Feature Attribution Stability Suite benchmarks post-hoc attribution methods for stability across perturbations with prediction-invariance...
Explainable Vision-Language Model for Lumbar Spinal Stenosis
Discover an explainable vision-language model using adaptive PID-Tversky loss to improve lumbar spinal stenosis diagnosis accuracy and interpretability.
Hierarchical Label-Free Concept Bottleneck Model Explained
Discover the Hierarchical Interpretable Label-Free Concept Bottleneck Model (HIL-CBM) that improves accuracy and interpretability in AI without labels.