Explain the world – Using causality to facilitate better rules for fuzzy systems

The rules of a rule-based system provide explanations for its behaviour 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 behaviour, but also communicate their ‘insights’ in respect to the real world. This requires rules to capture causal relationships between variables. In this paper, we argue that those systems where the rules reflect causal relationships between variables represent an important class of fuzzy rulebased 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 amongst a system's real-world variables to the human users of AI. We establish two causal-rule focused approaches to designing 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.