EVOTER: Evolution of Transparent Explainable Rule-sets
Hormoz Shahrzad, Babak Hodjat, Risto P Miikkulainen · ACM Transactions on Evolutionary Learning and Optimization · 2024
Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly met by these approaches. Various methods have therefore been developed to interpret black-box models after training. This article advocates an alternative approach where the models are transparent and explainable to begin with. This approach, EVOTER, evolves rule-sets based on extended propositional logic expressions. The approach is evaluated in several prediction/classification and prescription/policy search domains with and without a surrogate. It is shown to discover meaningful rule-sets that perform similarly to black-box models. The rules can provide insight into the domain and make hidden biases explicit. It may also be possible to edit the rules directly to remove biases and add constraints. EVOTER thus forms a promising foundation for building trustworthy AI systems for real-world applications in the future.