Explainable AI/ML Testing: Ensuring Transparency, Accountability, and Compliance
Praveen Kumar · Journal of Artificial Intelligence Machine Learning and Data Science · 2023
The increasing adoption of artificial intelligence (AI) and machine learning (ML) systems in critical domains such as healthcare, finance, and criminal justice has highlighted the need for explainable AI/ML models.Explainable AI/ML aims to provide transparency, accountability, and compliance by enabling users to understand how these systems make decisions.Testing explainable AI/ML systems presents unique challenges due to the complexity of the models, the need for human interpretability, and the ethical and legal implications of their decisions.This paper proposes a comprehensive testing framework for explainable AI/ML systems that addresses these challenges.The framework incorporates model interpretability testing, bias and fairness testing, robustness testing, and user experience testing.We also discuss the integration of domain expertise, ethical considerations, and regulatory compliance in the testing process.A case study is presented to demonstrate the application of the proposed framework in a real-world explainable AI/ML system for credit risk assessment.The results highlight the effectiveness of the framework in identifying interpretability issues, detecting biases, and ensuring compliance with regulations.The paper concludes with recommendations for implementing the testing framework and future research directions in explainable AI/ML testing.