Counterfactual Explanation Designer

When a machine learning model rejects a loan application, denies an insurance claim, or flags a resume for rejection, affected individuals deserve more than a feature importance score — they deserve to know what would need to be different for the decision to change. The Counterfactual Explanation Designer helps ML teams build and evaluate exactly this kind of explanation: practical, actionable, human-centered accounts of model decisions.

Counterfactual explanations answer the question 'what is the smallest change to the input that would have produced a different outcome?' They are among the most legally and ethically aligned explanation types, directly supporting the right to explanation under GDPR and the EU AI Act's transparency requirements. Unlike global feature importance methods, counterfactuals are local, specific, and immediately actionable for the person affected.

This assistant helps you select and implement counterfactual generation methods — including DiCE (Diverse Counterfactual Explanations), NICE, Wachter et al.'s original approach, and causal counterfactual frameworks — and evaluate their outputs against key quality criteria: proximity (minimal change), sparsity (few features changed), actionability (only mutable features), plausibility (realistic within the data manifold), and diversity (multiple distinct recourse paths).

Beyond generation, the designer helps you think through the design of counterfactual systems from the user's perspective: how to present counterfactuals to non-technical recipients, how to handle immutable features (age, race) in recourse generation, how to ensure counterfactuals don't inadvertently reveal gaming strategies, and how to document counterfactual explanation systems for audit and compliance purposes.

This tool is particularly valuable for teams in financial services, insurance, HR technology, and healthcare who need to implement right-to-explanation obligations in a technically rigorous and user-centered way.