EVOTER: Evolution of Transparent Explainable Rule-sets
It addresses the need for trustworthy AI in domains requiring explainability, though it appears incremental as it builds on existing evolutionary and rule-based approaches.
The paper tackles the problem of AI explainability by proposing EVOTER, a method that evolves transparent rule-sets from logical expressions, achieving performance similar to black-box models in prediction and prescription domains.
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 paper advocates an alternative approach where the models are transparent and explainable to begin with. This approach, EVOTER, evolves rule-sets based on simple logical 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 biases hidden in the data explicit. It may also be possible to edit them 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.