A Longitudinal Study of Trust Calibration Methods with Individual Differences
Abstract
This report summarizes our major research activities, study results and research accomplishments out of the trust calibration project in the past years. This is also the final report of the project. We have conducted different experiments on trust examination with varied system accuracy, and human trust in predictive decision making. From the study we have revealed that: 1) AI performance, in particular system errors, has a huge implication on human's trust adjustment and decision making. 2) Humans are able to perceive the detailed system performance not only at system level but also at subsystem level using systematic sampling strategies. 3) Trust knowledge acquired by human could be used to guide their future decision making. 4) The influence information of training data points (functioned as fact-checking) on predictions can benefit trust. 5) Personality traits affect trust in predictive decision making differently under different cognitive load levels. Our on-going work is focusing on refined quantification of human trust, and its implication on perception and decision making in the human-machine collaboration context.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 21, 2022
- Accession Number
- AD1175601
Entities
People
- Fang Chen
Organizations
- Commonwealth Scientific and Industrial Research Organisation