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The four dimensions of contestable AI diagnostics : A patient-centric approach to explainable AI
چهار بعد از تشخیص رقابتی هوش مصنوعی : یک رویکرد بیمار محور برای هوش مصنوعی قابل توضیح-2020 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. Keywords: AI diagnostics | Contestability | Explainability | Health data | Bias | Performance | Organisation of diagnostic labour |
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