Explanation Type

Explanation methods for machine learning models aim to clarify how these models arrive at their predictions, fostering trust and understanding. Current research focuses on comparing the effectiveness of different explanation types, such as those based on feature importance, rule sets, or graph structures, across various model architectures and evaluating their impact on user comprehension and trust. This work highlights the need for explanations that are not only technically sound but also easily interpretable by users with varying levels of expertise, impacting the responsible development and deployment of AI systems. A key challenge is balancing user preferences with objective measures of explanation quality.