Trust in automation is a foundational principle in Human Factors Engineering. An understanding of trust can help predict and alter much of human-machine interaction (HMI). However, despite the utility of assessing trust in automation in applied settings, there are inherent and unique challenges in trust assessment for those who seek to do so outside of the confines of the sterile lab environment. Because of these challenges, new approaches for trust in automation assessment need to be developed to best suit the unique demands of trust assessment in the real world. This paper lays out six requirements for these future measures: they should (1) be short, unobtrusive, and interaction-based, (2) be context-specific and adaptable, (3) be dynamic, (4) account for autonomy versus automation dependency, (5) account for task dependency, and (6) account for levels of risk. For the benefits of trust assessment to be realized in the "real world," future research needs to leverage the existing body of literature on trust in automation while looking toward the needs of the practitioner.
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