Explanation Format does not Matter; but Explanations do - An Eggsbert study on explaining Bayesian Optimization tasks

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Tanmay Chakraborty, Marion Koelle, Jörg Schlötterer, Nadine Schlicker, Christian Wirth, Christin Seifert

Published: 26 Dec 2025, Last Modified: 05 Jan 2026Information Systems FrontiersEveryoneRevisionsCC BY-SA 4.0
Abstract: Bayesian Optimization (BO) is a family of methods for finding optimal parameters when the underlying function to be optimized is unknown. BO is used, for example, for hyperparameter tuning in machine learning and as an expert support tool for tuning cyberphysical systems. For settings where humans are involved in the tuning task, methods have been developed to explain BO (Explainable Bayesian Optimization, XBO). However, there is little guidance on how to present XBO results to humans so that they can tune the system effectively and efficiently. In this paper, we investigate how the XBO explanation format affects participants’ task performance, task load, understanding and trust in XBO. We chose a task that is accessible to a wide range of participants. Specifically, we set up an egg cooking scenario with 6 parameters that participants had to adjust to achieve a perfect soft-boiled egg. We compared three different explanation presentation formats: a bar chart, a list of rules and a textual explanation in a between-subjects online study with 213 participants. Our results show that adding any format of explanation presentation increases task success, reduces the number of trials needed to achieve success, and improves perceived understanding and confidence. While explanations add more information for participants to process, we found no increase in user task load. We also found that the aforementioned results were independent of the explanation format; all formats had a similar effect. This is an interesting finding for practical applications, as it suggests that explanations can be added to BO tuning tasks without the burden of designing or selecting specific explanation formats. In the future, it would be interesting to investigate scenarios of prolonged use of the explanation formats and whether they have different effects on participants’ mental models of the underlying system.