Title
Interpretable confidence measures for decision support systems
Author
Waa, J.V.D.
Schoonderwoerd, T.
Diggelen, J.V.
Neerincx, M.
Publication year
2020
Abstract
Decision support systems (DSS) have improved significantly but are more complex due to recent advances in Artificial Intelligence. Current XAI methods generate explanations on model behaviour to facilitate a user's understanding, which incites trust in the DSS. However, little focus has been on the development of methods that establish and convey a system's confidence in the advice that it provides. This paper presents a framework for Interpretable Confidence Measures (ICMs). We investigate what properties of a confidence measure are desirable and why, and how an ICM is interpreted by users. In several data sets and user experiments, we evaluate these ideas. The presented framework defines four properties: 1) accuracy or soundness, 2) transparency, 3) explainability and 4) predictability. These characteristics are realized by a case-based reasoning approach to confidence estimation. Example ICMs are proposed for -and evaluated on- multiple data sets. In addition, ICM was evaluated by performing two user experiments. The results show that ICM can be as accurate as other confidence measures, while behaving in a more predictable manner. Also, ICM's underlying idea of case-based reasoning enables generating explanations about the computation of the confidence value, and facilitates user's understandability of the algorithm. © 2020 The Authors
Subject
Artificial intelligence
Confidence
Explainable AI
Interpretable
Interpretable machine learning
Machine learning
Transparency
Trust calibration
User study
Case based reasoning
Case-based reasoning approaches
Confidence estimation
Confidence Measure
Confidence values
Decision support system (dss)
Modeling behaviour
Multiple data sets
Understandability
To reference this document use:
http://resolver.tudelft.nl/uuid:2a05c19d-3e1c-411a-b086-c186fbafabb0
DOI
https://doi.org/10.1016/j.ijhcs.2020.102493
TNO identifier
878343
ISSN
1071-5819
Source
International Journal of Human Computer Studies, 144 (144)
Document type
article