The Role of Trust and Automation in an Intelligence Analyst Decisional Guidance Paradigm

Abstract

Trust in automation has been linked to a multitude of performance improvements and implicated in the reduction of human error, stress, and workload. In the present study, trust in automation was examined in an experiment measuring the efficacy of linguistic annotation schemes for decision support and human performance. An automated aid provided decisional guidance to assist in intelligence task performance. Four hundred and fifty-eight participants were randomly assigned to one of three annotation schemes and then subsequently performed three simulated intelligence analysis task. The results indicated that trust played a significant role in intelligence task performance, though a significant trust by annotation scheme interaction did not emerge. Specifically, an increase in trust accompanied an increase in performance across the task types. We conclude with a discussion of trust and automated annotation schemes, which has implications for the intelligence operations community.