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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 / Conférence
External document link
https://hal.archives-ouvertes.fr/hal-02890002
Date
2020
Conference title
Information Processing and Management of Uncertainty in Knowledge-Based Systems. 18th International Conference, IPMU 2020
Conference date
2020
Book author
Lesot, M.-J., Vieira, S., Reformat, M.
Publisher
Springer
Pages
99-113
Publication identifier
10.1007/978-3-030-50153-2_8
Metadata
Show full item record
Author(s)
Richard, Antoine
Mayag, Brice
Talbot, François
Tsoukiàs, Alexis cc
Meinard, Yves
Abstract (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 Classification

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