Tag: explainable AI
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Ensuring Fairness in AI Explanations: Framework & Future
Explore a unifying framework and key principles to ensure fairness in AI explanations, advancing responsible and transparent artificial intelligence.
Explainable Knowledge Tracing with Probabilistic Embeddings
Discover how Probabilistic Logical Knowledge Tracing enhances student performance prediction with interpretable, goal-conditioned reasoning.
Bridging Consistency-Based Diagnosis with Actual Causality
Explore how linking Consistency-Based Diagnosis with Actual Causality enhances Explainable AI for transparent, accountable, and reliable decision-making.
Robust Explainability for Safety-Critical ATR Systems
Explore limitations of post-hoc XAI methods in ATR systems and discover paths toward robust, reliable explainability for safety-critical AI applications.
Learning to Theorize: AI Understanding Through Observation
Discover how AI advances by learning to theorize the world from observations, enhancing understanding beyond mere predictions with novel cognitive models.