Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stake

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ethics.ai (19 October 2022), “Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review,” evidence record 9200, https://ethics.ai/record/9200 (originally published by OpenAlex).

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