TrustyAI Explainability Toolkit

TL;DR

The paper introduces the TrustyAI Explainability Toolkit, a library that provides explainability solutions for AI models and decision services, enhancing transparency in complex AI systems.

Contribution

It presents the design and implementation of a new explainability toolkit with extensions to LIME, SHAP, and counterfactual methods, benchmarked against existing solutions.

Findings

The toolkit effectively explains AI decision services and models.

Extensions improve explanation accuracy and usability.

Benchmark results show competitive performance.

Abstract

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.