Tailored Explainability in Medical Artificial Intelligence-empowered Applications: Personalisation via the Technology Acceptance Model

Abstract

The great momentum of Artificial Intelligence-empowered applications makes the requirement for detailed and tailored explainability frameworks more crucial. This is particularly evident in the medical domain, where validation of methodologies and outcomes is very important to the adoption of such systems. The depth and the level of understanding of Artificial Intelligence-related concepts is a significant design parameter and necessitates a systemic approach to ensure that a proper level of transparency is incorporated in an Artificial Intelligence-empowered application. In this paper, we propose a novel and generalised approach for the analysis of user requirements and abilities in relation to Artificial Intelligence-empowered applications. Specifically, we use the Technology Acceptance Model (TAM), as a technical methodology to measure the user perception of usefulness and usability of a technology and, subsequently, identify the corresponding depths of explainablity requirements. As a result, we design a layered and personalised explainability framework that may increase adoption rates of domain-specific Artificial Intelligence-empowered technologies.

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