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.

翻译：大多数人工智能系统是黑盒，能为给定输入生成合理输出。然而，某些领域对可解释性和可信赖性有特定要求，这些方法无法直接满足。因此，人们开发了多种方法来在训练后解释黑盒模型。本文倡导一种替代方案：从初始阶段即构建透明且可解释的模型。该方法（EVOTER）基于简单逻辑表达式演化规则集。研究在多个预测/分类以及决策/策略搜索领域中（无论是否使用代理模型）对该方法进行了评估。结果表明，该方法能发现具有意义的规则集，其性能与黑盒模型相当。这些规则能够提供对领域的深入理解，并显式揭示数据中隐藏的偏差。此外，还可能通过直接编辑规则来消除偏差并添加约束。因此，EVOTER为未来构建面向实际应用的可信赖人工智能系统奠定了有前景的基础。