The four dimensions of contestable AI diagnostics - A patient-centric approach to explainable AI

Abstract

The problem of the explainability of AI decision-making has attracted considerable attention in recent years. In considering AI diagnostics we suggest that explainability should be explicated as 'effective contestability'. Taking a patient-centric approach we argue that patients should be able to contest the diagnoses of AI diagnostic systems, and that effective contestation of patient-relevant aspect of AI diagnoses requires the availability of different types of information about 1) the AI system's use of data, 2) the system's potential biases, 3) the system performance, and 4) the division of labour between the system and health care professionals. We justify and define thirteen specific informational requirements that follows from 'contestability'. We further show not only that contestability is a weaker requirement than some of the proposed criteria of explainability, but also that it does not introduce poorly grounded double standards for AI and health care professionals' diagnostics, and does not come at the cost of AI system performance. Finally, we briefly discuss whether the contestability requirements introduced here are domain-specific.

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