Assessing AI Explainability: A Usability Study Using a Novel Framework Involving Clinicians

Abstract

An AI design framework was developed based on three core principles, namely understandability, trust, and usability. The framework was conceptualized by synthesizing evidence from the literature and by consulting with experts. The initial version of the AI Explainability Framework was then validated based on an in-depth expert engagement and review process. For evaluation purposes, an AI-anchored prototype, incorporating novel explainability features, was built and deployed online via Google Cloud Platform. The primary function of the prototype was to predict the postpartum depression risk using analytics models. The development of the prototype was carried out in an iterative fashion, based on a pilot-level formative evaluation, followed by a round of refinement and summative evaluation. In the formative stage, the prototype was evaluated based on an internal pilot usability test involving a small number of clinicians (n=3). The prototype was updated based on the user’s feedback in the formative stage. The System Explainability Scale (SES) metric was developed to measure the individual and interacting influence of the three dimensions of the AI Explainability Framework. For the summative stage, a comprehensive usability test was conducted involving 20 clinicians, and the SES metric was used to assess clinicians’ satisfaction with the tool. On a 5-point rating system, the tool received high scores for usability dimension, followed by trust and understandability. The average explainability score was 4.56. In terms of understandability, trust and usability, the average score was 4.51, 4.53, and 4.71 respectively. Overall, the 13-item SES metric showed strong internal consistency with Cronbach’s alpha of 0.84 and a positive correlation coefficient (Spearman's rho = 0.81, p<0.001) between the composite SES score and explainability, indicating a positive trend in AI explainability. This study demonstrated the influence of understandability, trust, and usability on AI Explainability using a combination of a novel design and experimental approach. A major finding was that the AI Explainability Framework, combined with the SES usability metric, provides a straightforward yet effective approach for developing AI-based healthcare tools that lower the challenges associated with explainability.

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