Transparency of Classification Systems for Clinical Decision Support

Antoine Richard (1) , Brice Mayag (1) , François Talbot , Alexis Tsoukiàs (1) , Yves Meinard (1)

1 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision

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.

Domaines: Intelligence artificielle [cs.AI]; Autre [cs.OH]

Antoine Richard, Brice Mayag, François Talbot, Alexis Tsoukiàs, Yves Meinard. Transparency of Classification Systems for Clinical Decision Support. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part III, pp.99-113, 2020, Communications in Computer and Information Science, ⟨10.1007/978-3-030-50153-2_8⟩. ⟨hal-02890002⟩