Web: http://arxiv.org/abs/2112.12596

Sept. 16, 2022, 1:12 a.m. | Haomin Chen, Catalina Gomez, Chien-Ming Huang, Mathias Unberath

cs.LG updates on arXiv.org arxiv.org

Transparency in Machine Learning (ML), attempts to reveal the working
mechanisms of complex models. Transparent ML promises to advance human factors
engineering goals of human-centered AI in the target users. 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 user; as a
result, iterative prototyping and evaluation with users is critical to
attaining adequate solutions that afford transparency. However, following
human-centered design principles in healthcare and …

analysis arxiv evidence guidelines human image medical review

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