On the Interpretability and Explainability of Prototype-Based Methods and Reinforcement Learning
Seyed Omid Davoudi · 2024
With the ever-growing use of AI to solve real-world problems, the need for transparency and trust in these methods has given rise to Interpretable and Explainable AI.While a growing body of research is trying to address these issues, some areas of the field can still be further improved.In the field of inherently interpretable AI methods, the embedding spaces used for the current prototype-based neural network methods have issues with prototype-query similarity.In the same area of prototype-based neural networks, another issue is the inadequacies of current schemes for evaluating the interpretability of part-prototype networks.In addition, in the field of reinforcement learning, post-hoc explainability methods are focused more on policy distillation rather than on local feature-based explanations.In this work, an inherently interpretable method is proposed to create more interpretable prototypes in a prototype-based classification scheme.In addition, a robust human-centric evaluation framework is proposed for part-prototype networks.Another method is also proposed to create posthoc explainability in reinforcement learning by utilizing state features.Experiments show the superiority and validity of these methods compared to the previous state of the art.In the case of the proposed evaluation metric, this work also includes a comprehensive comparison of the interpretability of existing part-prototype-based neural network methods.