Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering

Concept-based explanations have emerged as an effective approach within Explainable Artificial Intelligence, enabling interpretable insights by aligning model decisions with human-understandable concepts. However, existing methods rely on computationally intensive procedures and struggle to efficiently capture complex, semantic concepts. This work introduces the Concept Directions via Latent Clustering (CDLC), which extracts global, class-specific concept directions by clustering latent difference vectors derived from factual and diffusion-generated counterfactual image pairs. CDLC reduces storage requirements by 4.6 and accelerates concept discovery by 5.3 compared to the baseline method, while requiring no GPU for clustering, thereby enabling efficient extraction of multidimensional semantic concepts across latent dimensions. This approach is validated on a real-world skin lesion dataset, demonstrating that the extracted concept directions align with clinically recognized dermoscopic features and, in some cases, reveal dataset-specific biases or unknown biomarkers. These results highlight that CDLC is interpretable, scalable, and applicable across high-stakes domains and diverse data modalities. Introduction In high-stakes applications, such as medical diagnosis, financial risk assessment, and autonomous driving, understanding the rationale behind a neural network's decision is often as important as the decision itself. Explainable Artificial Intelligence (XAI) [1, 2] has emerged as a critical research area, aiming to bridge the gap between high-performing black-box models and human interpretability. Among the various XAI paradigms, concept-based explanations [3, 4] have gained particular attention due to their ability to express model behavior in terms of high-level, semantically meaningful concepts, rather than low-level feature weights or pixel-based saliency maps [5, 6]. By aligning explanations with concepts recognized by domain experts, these methods facilitate trust [7, 8], debugging [9], and regulatory compliance [10, 11].