Transparency of Classification Systems for Clinical Decision Support
Richard, Antoine; Mayag, Brice; Talbot, François; Tsoukiàs, Alexis; Meinard, Yves (2020), Transparency of Classification Systems for Clinical Decision Support, Information Processing and Management of Uncertainty in Knowledge-Based Systems. 18th International Conference, IPMU 2020, 2020
Type
Communication / ConférenceExternal document link
https://hal.archives-ouvertes.fr/hal-02890002Date
2020Conference title
Information Processing and Management of Uncertainty in Knowledge-Based Systems. 18th International Conference, IPMU 2020Conference date
2020Book author
Lesot, M.-J., Vieira, S., Reformat, M.Publisher
Springer
Pages
99-113
Publication identifier
Metadata
Show full item recordAbstract (EN)
In collaboration with the Civil Hospitals of Lyon, we aim to develop a "transparent" classification system for medical purposes. To do so, we need clear definitions and operational criteria to determine what is a "transparent" classification system in our context. However, the term "transparency" is often left undefined in the literature, and there is a lack of operational criteria allowing to check whether a given algorithm deserves to be called "transparent" or not. Therefore, in this paper, we propose a definition of "transparency" for classification systems in medical contexts. We also propose several operational criteria to evaluate whether a classification system can be considered "transpar-ent". We apply these operational criteria to evaluate the "transparency" of several well-known classification systems.Subjects / Keywords
Explainable AI; Transparency of Algorithms; Health Information Systems; Multi-label ClassificationRelated items
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