Separating Rule Discovery and Global Solution Composition in a Learning Classifier System
This addresses the need for explainable AI in decision-making scenarios, though it appears incremental as it builds on existing rule-based methods with a specific structural innovation.
The paper tackles the problem of trust in AI decision-making by proposing a rule-based learning system designed for inherent interpretability, achieving transparency by evolving rule conditions and solution composition separately.
While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to profit from their application, resulting in a need for explanations for both the decision making process and the model. For many systems, such as common black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. We propose a rule-based learning system specifically conceptualised and, thus, especially suited for these scenarios. Its models are inherently transparent and easily interpretable by design. One key innovation of our system is that the rules' conditions and which rules compose a problem's solution are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability.