Quality Explanation

Quality explanation in artificial intelligence (AI) focuses on generating understandable and trustworthy justifications for AI model predictions, addressing the critical need for transparency and accountability, especially in high-stakes domains like healthcare. Current research emphasizes developing methods to assess explanation quality, including metrics for faithfulness, utility, and human understandability, often employing large language models (LLMs) and techniques like chain-of-thought prompting and iterative refinement to generate more effective explanations. This work is crucial for building trust in AI systems and enabling responsible deployment across various applications, improving both model interpretability and user confidence in AI-driven decisions.