  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.
