  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.
