Reason to explain: Interactive contrastive explanations (REASONX)

Interactive explanations improve understanding: REASONX enables dialogue-like interaction with AI explanations, allowing users to ask follow-up questions and explore different aspects of a model's decision—moving beyond static explanations that may not address what users actually want to know.

Background knowledge makes explanations more relevant: By incorporating domain-specific background knowledge, the tool can tailor explanations to individual user needs and contexts, potentially making AI decisions more understandable and trustworthy to the people affected by them.

Explanations work for any AI model type: REASONX can explain not just simple decision trees, but also complex "black box" models by creating simpler surrogate models—addressing a key challenge in responsible AI where the most powerful models are often the hardest to understand.

Multiple explanation types provide flexibility: The tool generates different kinds of explanations (factual rules, contrastive examples, and decision rules), giving users multiple ways to understand why an AI system made a particular decision rather than forcing a one-size-fits-all approach.

Technical accessibility matters for adoption: By providing both a specialized programming layer and a Python interface, REASONX makes explanation tools available to practitioners without deep technical expertise, potentially enabling broader implementation of interpretability in real-world decision-making systems.