Outputs (2)

A python library for data-driven causal fuzzy classification rule generation --mablars (2025)
Presentation / Conference Contribution
Zhang, T., & Wagner, C. (2025, July). A python library for data-driven causal fuzzy classification rule generation --mablars. Presented at 2025 IEEE International Conference on Fuzzy Systems, Reims, France

A principal focus in fuzzy systems research is on maintaining good performance while providing strong explain-ability, principally by leveraging meaningful sets of human-accessible rules. In practice, while a variety of software tools have been devel... Read More about A python library for data-driven causal fuzzy classification rule generation --mablars.

Counterfactual linguistic rule-based explanations based on locally relevant causal mechanisms (2025)
Presentation / Conference Contribution
Zhang, T., & Wagner, C. (2025, July). Counterfactual linguistic rule-based explanations based on locally relevant causal mechanisms. Presented at 2025 IEEE International Conference on Fuzzy Systems, Reims, France

Counterfactual (CF) explanations provide a potentially powerful mechanism to deliver meaningful explanations of AI decisions. CF explanations are convincing when they reflect causal relationships between variables, because humans are cause-effect thi... Read More about Counterfactual linguistic rule-based explanations based on locally relevant causal mechanisms.