Interpretable AI: Beyond Post-Hoc Explanations Towards Inherently Understandable Models

Abstract

This paper explores the evolving landscape of interpretable artificial intelligence (AI), focusing on the shift from post-hoc explainability methods to inherently understandable models. As AI systems become increasingly integrated into critical decision-making processes across various domains, the need for transparency and trustworthiness becomes paramount. Traditional post-hoc explanation techniques often provide explanations after the model has made a prediction, which can be limited by the inherent complexity of the underlying model. This work advocates for the development and utilization of models that are interpretable by design, offering insights into their decision-making processes from the outset. We investigate various approaches to building inherently interpretable models, including linear models, decision trees, rule-based systems, and attention mechanisms. We analyze their strengths and limitations, focusing on their ability to provide transparent and understandable reasoning. Furthermore, the paper examines the trade-offs between model complexity, accuracy, and interpretability, highlighting the importance of selecting the appropriate model for specific application domains. The goal is to promote the adoption of interpretable AI methods that empower users to understand, trust, and effectively utilize AI systems in a wide range of real-world scenarios.