Explain the world --- towards leveraging causality in fuzzy rule based systems - Zhang, Te (2025)
Artificial intelligence (AI) is increasingly applied across sectors, including risk-sensitive areas such as healthcare and security, driving the growing demand for explainable AI (XAI). Among various XAI approaches, fuzzy rule-based systems provide a tempting architecture for XAI, offering linguistic, human-accessible rules, combined with the capacity for handling complex applications amid varying levels of uncertainty, vagueness and imprecision. Nowadays, rules are frequently obtained through data-driven approaches. However, while these systems explain model behaviour by revealing the relationships between the variables captured, they often capture correlations. Ideally, AI systems are expected to go beyond explainable AI (XAI), that is, systems which not only explain their behaviour, but also communicate their `insights' in respect to the real world. Thus, rules are expected to capture causal relationships between variables.