Explain the World—Using Causality to Facilitate Better Rules for Fuzzy Systems

Abstract

The rules of a rule-based system provide explanations for its behavior by revealing the relationships between the variables captured. However, ideally, we have AI systems which go beyond explainable AI (XAI), that is, systems which not only explain their behavior, but also communicate their “insights” with respect to the real world. This requires rules to capture causal relationships between variables. In this article, we argue that those systems where the rules reflect causal relationships between variables represent an important class of fuzzy rule-based systems with unique benefits. Specifically, such systems benefit from improved performance and robustness; facilitate global explainability and thus cater to a core ambition for AI: the ability to communicate important relationships among a system's real-world variables to the human users of AI. We establish two causal-rule focused approaches to design fuzzy systems, and show the distinctions in their respective application scenarios for the explanations of the rules obtained by these two methods. The results show that rules which reflect causal relationships are more suitable for XAI than rules which “only” reflect correlations, while also confirming that they offer robustness to over-fitting, in turn supporting strong performance.