Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion
This addresses the need for interpretable AI in structured modalities, offering a novel plug-and-play solution for building user trust, though it is incremental in applying diffusion models to a new domain.
The paper tackles the problem of generating counterfactual explanations for black-box neural network models in structured data, introducing SCD as a diffusion-based framework that achieves higher plausibility, proximity, and diversity compared to state-of-the-art methods.
Generating counterfactual explanations is one of the most effective approaches for uncovering the inner workings of black-box neural network models and building user trust. While remarkable strides have been made in generative modeling using diffusion models in domains like vision, their utility in generating counterfactual explanations in structured modalities remains unexplored. In this paper, we introduce Structured Counterfactual Diffuser or SCD, the first plug-and-play framework leveraging diffusion for generating counterfactual explanations in structured data. SCD learns the underlying data distribution via a diffusion model which is then guided at test time to generate counterfactuals for any arbitrary black-box model, input, and desired prediction. Our experiments show that our counterfactuals not only exhibit high plausibility compared to the existing state-of-the-art but also show significantly better proximity and diversity.