Concept-based Visual Counterfactual Explanations with Diffusion Models

Concept-based explanations improve transparency: Rather than showing pixel-level changes that are hard to interpret, C-VCE uses human-understandable semantic concepts (like "age" or "expression" in facial images), making it easier for non-experts to understand why an AI model made a particular decision.

Smaller, more realistic edits build trust: The method produces counterfactual images that are visually closer to the original and less distorted than existing approaches, which is critical for safety-critical applications like medical imaging where doctors need to trust that explanations reflect realistic scenarios.

Built-in interpretability reduces deployment risks: By embedding the classifier directly into the generative model rather than relying on separate external classifiers, C-VCE eliminates a fragile component that could fail on noisy or edited images—a practical advantage for deploying explainable AI systems in real-world settings.

Targeted edits preserve image integrity: The framework uses a gradient-based mask to confine modifications only to the most relevant regions while preserving the rest of the image and respecting how features naturally correlate, preventing unrealistic or misleading explanations.

Practical tool for high-stakes decisions: By providing concrete "what-if" visualizations that show minimal changes needed to flip a model's prediction, C-VCE helps stakeholders in safety-critical domains (medicine, autonomous systems) understand and audit AI decisions before deployment.