TRACE: A Dynamic Model of Trust for People-Driven Service Engagements

Trust is an important element of achieving secure collaboration that deals with human judgment and decision making. We consider trust as it arises in and influences people-driven service engagements. Existing approaches for estimating trust between people suffer from two important limitations. One, they consider only commitment as the primary means of estimating trust and omit additional significant factors, especially risk and emotions. Two, they typically estimate trust based either on fixed parameter models that require manual setting of parameters or based on Hidden Markov Models (HMM), which assume conditional independence and are thus ill-suited to capturing complex relationships between trust, risk, commitments, and emotions. We propose TRACE, a model based on Conditional Random Fields (CRF) that predicts trust from risk, commitments, and emotions. TRACE does not require manual parameter tuning and relaxes conditional independence assumptions among input variables. We evaluate TRACE on a dataset collected by the Intelligence Advanced Research Projects Activity (IARPA) in a human-subject study. We find that TRACE outperforms existing trust-estimation approaches and that incorporating risk, commitments, and emotions yields lower trust prediction error than incorporating commitments alone.