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Concept Visualization for Enhanced Explainability in Tabular Deep Learning

CORDIS · observation · Publication date unknown

Concept Visualization for Enhanced Explainability in Tabular Deep Learning The EU's GDPR and the new EU AI Act impose strict requirements on AI systems, especially in high-risk domains like healthcare. These regulations demand transparency and meaningful explanations for automated decisions to ensure adequate human oversight. State-of-the-art eXplainable AI (XAI) methods, such as LIME and SHAP, attempt to meet these demands with low-level explanations. However, these methods often yield inconsistent and decoupled results from high-level concepts that domain experts like physicians use in decision making. In contrast, Deep Learning (DL) models that generate concept-based explanations during training offer more robust and consistent outcomes. Despite their promise, concept-oriented DL models for tabular data remain underdeveloped, with the recent Tabular Concept Bottleneck Models (TabCBMs)—a family of interpretable, self-explaining DL models designe

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recordType

award

status

SIGNED

region

EU

value

232916.16

unit

EUR