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Abstract

The study introduces influence, an information-theoretic measure based in dynamical systems theory to capture the spread of trust and distrust in human-AI teams (HATs) over time. Influence, calculated using average mutual information, captures how joint teammate actions affect system-level states. Forty-five three-member teams completed five 40-min missions. Participants acted as photographers alongside two confederates in navigator and pilot roles who portrayed either human or AI teammates. Trust and distrust were spread communicatively by the navigator (between-subjects) and behaviorally by the pilot (within-subjects). Three influence time series were computed per teammate pair per mission and used in a series of repeated measures multiple regressions to predict individual performance and self-reported trust (i.e., team trustworthiness; cognitive and affective trust). Influence predicted team trustworthiness in the control condition and affective trust in the pilot in the communicative trust spreading condition. Results suggest influence is sensitive to both communicative and behavioral spread in HATs.

Keywords

human-AI teaming trust average mutual information dynamical systems performance distrust trust model dynamic trust

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A Dynamic Trust and Distrust Influence Metric that Predicts Team Trustworthiness and Affective Trust in Human Teams and Human-AI Teams

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