Emotional expressions facilitate human–human trust when using automation in high-risk situations

ABSTRACT

This study investigated the utility of emotional expression for human decision aids, when human aids conflict with an automated decision support system (DSS). The increasing presence of automation in society has resulted in critical, and often life threatening, situations when information from human and automated sources disagree. It has been known that reliance on human aids decrease during high-risk situations, while reliance on automated aids increase. However, it is also possible that human decision aids gain credibility from users when they embody the charismatic and emotionally expressive gesticulations seen in successful organizational leaders. The present study tested how a human agent's expressiveness when providing information would influence participants' behavioral reliance. Using the program Convoy Leader, participants (n=56) engaged in three decision-making scenarios where risk was manipulated as a within-subject factor and emotional expression as a between-subject factor. Emotional susceptibility, perceived risk, and trust for human as well as automated aids were measured. Overall trust was higher for the automated tool than human decision aid, and that pattern was amplified in conditions without an emotionally expressive human aid. Reliance was greater for emotionally expressive human aids, than stoic human aids, particularly during high risk conditions. The findings suggest that emotional expression of a human aid significantly impacts both reliance and trust of a decision aid, especially at higher risk levels. Emotionally expressive human agents should be utilized in decision conflicts where the automated system has certainly failed.

KEYWORDS:

• Trust in automation
• human–human reliance
• emotional expression
• risk perception
• human-automation integration

Acknowledgments

This work was supported in part by the National Security Agency (NSA)-funded Laboratory for Analytic Sciences (LAS) at North Carolina State University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Laboratory for Analytic Sciences.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Laboratory for Analytic Sciences (LAS), grant No. H98230-13-D-0054.