Abstract
This paper proposes a conceptual model derived through current ongoing research that incorporates the potential relationship that mental health may have on trust and reliance calibration in automated systems (AS). Understanding the variables involved in the human-AS interaction allows system designers to better achieve trust calibration and avoid AS misuse and disuse. However, most of the research area is saturated with understanding how external and internal (both to the human) short-term cognitive symptoms mediate this critical relationship. Therefore, the present paper extends human-AS trust literature with common mental disorders (CMDs) as outlined by the Adult Psychiatric Morbidity Survey (APMS) and incorporates them into existing models within the engineering and psychology subject areas to begin to understand what this relationship may look like. It is hoped that this paper will expand the scope of human factors (specifically human-AS trust) to include mental disorders.
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Crawford, J.R., Hubbard, EM., Goh, Y.M. (2020). Mental Health, Trust, and Robots: Towards Understanding How Mental Health Mediates Human-Automated System Trust and Reliance. In: Ayaz, H. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-030-20473-0_13
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