Are you sure? Prediction revision in automated decision‐making. Issue 1 (12th June 2020)

• Record Type: Journal Article
• Authors: Burkart, Nadia; Robert, Sebastian; Huber, Marco F.
• Other Names: Gupta Deepak guestEditor.; Rodrigues Joel J. P. C. guestEditor.; Castillo Oscar guestEditor.; Herrero Álvaro guestEditor.; Jiménez Alfredo guestEditor.; Bayraktar Secil guestEditor.; Arroyo Angel guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-12
• Subjects: experiment -- explainable ML -- interpretability -- prediction revision; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12577
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between‐subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.