Towards a Conversational Measure of Trust
This addresses the need for continuous and unobtrusive trust measurement in collaborative human-agent decision-making, though it appears incremental as a complementary method.
The paper tackled the problem of measuring trust in human-agent interactions by proposing a nondirective and relational conversational approach, which unobtrusively elicits rich and dynamic trust information to complement traditional surveys.
The increasingly collaborative decision-making process between humans and agents demands a comprehensive, continuous, and unobtrusive measure of trust in agents. The gold standard format for measuring trust, a Likert-style survey, suffers from major limitations in dynamic human-agent interactions. We proposed a new approach to evaluate trust in a nondirective and relational conversation. The term nondirective refers to abstract word selections in open-ended prompts, which can probe respondents to freely describe their attitudes. The term relational refers to interactive conversations where respondents can clarify their responses in followup questions. We propose a systematic process for generating nondirective trust-based prompts by using text analysis from previously validated trust scales. This nondirective and relational approach provides a complementary trust measurement, which can unobtrusively elicit rich and dynamic information on situational trust throughout a human-agent interaction.