@ARTICLE{10.21494/ISTE.OP.2024.1239, TITLE={Trust and automation bias: differences between novices and experts in a military context}, AUTHOR={Anne-Lise Marchand , Nicolas Maille , Pauline Munoz , Laurent Chaudron, }, JOURNAL={Cognitive Engineering}, VOLUME={7}, NUMBER={Issue 2}, YEAR={2024}, URL={https://openscience.fr/Trust-and-automation-bias-differences-between-novices-and-experts-in-a-military}, DOI={10.21494/ISTE.OP.2024.1239}, ISSN={2517-6978}, ABSTRACT={This study examines whether the automation bias in situations of arbitration between human and AI-based assistance varies as a function of individuals’ psychosocial characteristics. The literature highlights the robustness of the automation bias in decision-making situations with a single aid, but a few recent studies mobilizing the dual decision aid paradigm identify more nuanced results, particularly as a function of participants’ characteristics. 2 groups of participants (37 military pilot students vs. 37 operational pilots) are engaged in an close air support mission simulation, where they must choose between information provided by a human aid and that provided by an AI-based automated aid. Trust in these aids is induced a priori by predefined levels of reliability (20%, 50%, 70% 90%). With equal reliability, when young participants and experts are confronted with a human aid and an AI-based aid, they have a preference for the human aid. However, this preference is greater for experts. The study questions the invariability of automation bias, highlighting the impact of the operator’s psychosocial characteristics on decision-making. It seems necessary to reconsider the automation bias in modern contexts through individual representations of technologies to optimize the design of decision support systems.}}