Artificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. The decisions made by such "black box" systems are often opaque; that is, so complex as to be functionally impossible to understand. How do we ensure that these systems are behaving as desired? TrustyAI is an initiative which looks into explainable artificial intelligence (XAI) solutions to address this issue of explainability in the context of both AI models and decision services. This paper presents the TrustyAI Explainability Toolkit, a Java and Python library that provides XAI explanations of decision services and predictive models for both enterprise and data science use-cases. We describe the TrustyAI implementations and extensions to techniques such as LIME, SHAP and counterfactuals, which are benchmarked against existing implementations in a variety of experiments.

翻译：人工智能(AI)越来越受欢迎,可以在世界各地的工作场所和家庭中找到。这种“黑盒子”系统所作的决定往往不透明,因此在功能上是无法理解的。我们如何确保这些系统如愿以偿?信任AI是一项研究可解释的人工智能(XAI)解决方案的倡议,旨在解决在AI模式和决策服务方面解释性问题。本文件介绍了信任AI解释工具箱、一个为企业和数据科学使用案例提供决策服务和预测模型的Java和Python图书馆。我们介绍了信任AI实施和扩展LIME、SHAP和反事实等技术的情况,这些技术是参照各种实验中的现有实施情况加以衡量的。