TrustyAI Explainability Toolkit
This toolkit aims to improve transparency and trust in AI systems for enterprise and data science users, but it appears incremental as it builds on established XAI methods.
The paper introduces the TrustyAI Explainability Toolkit, a Java and Python library that provides explainable AI (XAI) solutions for decision services and predictive models, addressing the opacity of 'black box' AI systems by implementing and extending techniques like LIME, SHAP, and counterfactuals, with benchmarking against existing implementations in experiments.
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.