Semantic Prototypes: Enhancing Transparency Without Black Boxes
This addresses the need for transparency in complex ML models for users and practitioners, though it appears incremental as it builds on existing prototype methods.
The paper tackles the problem of enhancing explainability and interpretability in machine learning by introducing a framework that uses semantic descriptions to define prototypes, outperforming existing methods in facilitating human understanding and informativeness as validated through a user survey.
As machine learning (ML) models and datasets increase in complexity, the demand for methods that enhance explainability and interpretability becomes paramount. Prototypes, by encapsulating essential characteristics within data, offer insights that enable tactical decision-making and enhance transparency. Traditional prototype methods often rely on sub-symbolic raw data and opaque latent spaces, reducing explainability and increasing the risk of misinterpretations. This paper presents a novel framework that utilizes semantic descriptions to define prototypes and provide clear explanations, effectively addressing the shortcomings of conventional methods. Our approach leverages concept-based descriptions to cluster data on the semantic level, ensuring that prototypes not only represent underlying properties intuitively but are also straightforward to interpret. Our method simplifies the interpretative process and effectively bridges the gap between complex data structures and human cognitive processes, thereby enhancing transparency and fostering trust. Our approach outperforms existing widely-used prototype methods in facilitating human understanding and informativeness, as validated through a user survey.