2 papers
cs.AI2025
A Framework for Causal Concept-based Model Explanations
Anna Rodum Bjøru, Jacob Lysnæs-Larsen, Oskar Jørgensen +2
This work presents a conceptual framework for causal concept-based post-hoc Explainable Artificial Intelligence (XAI), based on the requirements that explanations for non-interpret…
cs.AI2025
Probing the Probes: Methods and Metrics for Concept Alignment
Jacob Lysnæs-Larsen, Marte Eggen, Inga Strümke
In explainable AI, Concept Activation Vectors (CAVs) are typically obtained by training linear classifier probes to detect human-understandable concepts as directions in the activa…