Explainable Object Classification: Integrating Object Parts/Attributes and Expertise

While AI's accuracy is impressive, it often operates opaquely, leaving users puzzled by its decisions. Explainable AI (XAI) seeks to demystify these processes, yet it encounters usability hurdles, often favouring developers over end-users. This paper introduces EXPERT-DUO, a flexible framework for Explainable Object Classification. While demonstrated in the domain of surgical tool classification, EXPERT-DUO is a versatile system applicable across domains. Operating as an assistant system for the users, the framework accommodates varying levels of domain knowledge and provides understandable decisions through a hierarchical methodology. The framework pipeline starts by segmenting the object parts, recognizing and classifying the object parts that make up the main object, progresses to attribute classification, and culminates in the classification of the complete object using an expert decision tree that encodes the domain knowledge. EXPERT-DUO aims to assist users by offering transparent and understandable reasoning for the object classifications. This unique approach enables users to make rational and informed judgments regarding their trust in the model's decisions. Experimental results within the surgical context demonstrate the effectiveness of the approach. These results underscore EXPERT-DUO's potential to enhance user confidence in AI systems across a spectrum of domains, thereby facilitating more widespread adoption and utilization of AI technologies.