Towards Trustworthy and Understandable AI: Unraveling Explainability Strategies on Simplifying Algorithms, Appropriate Information Disclosure, and High-level Collaboration

Abstract

Human-centered artificial intelligence (AI) has garnered significant attention. Explainability strategies based on the concept of explainable AI (XAI) are comprehensive sets of techniques and principles that help users establish understandable and trustworthy AI systems. However, existing explainability strategies still face numerous challenges in enabling users to understand AI system decisions better. This literature review aims to explore how to overcome these challenges through simplified algorithms, appropriate information disclosure, and high-level collaboration, thereby offering future research direction for building AI systems that are trustworthy and understandable to users.