Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past two decades. The majority of prior literature adopted a "snapshot" view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This "snapshot" view, however, does not acknowledge that trust is a time-variant variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model based on Beta distribution and learn its parameters using Bayesian inference. Our proposed model adheres to three major properties of trust dynamics reported in prior empirical studies. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a Root Mean Square Error (RMSE) of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinctive types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.
When Robots Say No: Temporal Trust Recovery Through Explanation
School of Engineering Mathematics and Technology, University of Bristol, UK（布里斯托大学工程数学与科技学院）；Bristol Digital Futures Institute, University of Bristol, UK（布里斯托数字未来研究所）；School of Computer Science, University of Birmingham, UK（伯明翰大学计算机科学学院）
Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
Virginia Tech（弗吉尼亚理工大学）；C. G. Woodson High School（C. G. Woodson高中）；US DEVCOM Army Research Laboratory（美国国防高级研究计划局陆军实验室）；Korea Institute of Energy Technology (KENTECH)（韩国能源技术研究所）；The University of Queensland（昆士兰大学）
Predicting Trust Dynamics with Dynamic SEM in Human-AI Cooperation
Department of Informatics, The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan（信息学系，高级研究大学，SOKENDAI，东京，日本）；Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan（数字内容与媒体科学研究部，信息机构国家，东京，日本）
A study on human-agent teaming through spoken interaction: the impact of human individual traits and agent characteristics
Instituto de Investigación en Ciencias de la Computación, UBA-CONICET（布宜诺斯艾利斯大学-国家科学技术研究委员会计算机科学研究所）；Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires（布宜诺斯艾利斯大学精确与自然科学学院计算机系）；Universidad Nacional de Córdoba（科尔多瓦国立大学）；Instituto de Educación, Universidad Nacional de Hurlingham, UNAHUR-CONICET（Hurlingham国立大学-国家科学技术研究委员会教育研究所）
Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
MIT Lincoln Laboratory（麻省理工学院林肯实验室）；MIT Department of Electrical Engineering and Computer Science（麻省理工学院电气工程与计算机科学系）；U.S. Air Force Artificial Intelligence Accelerator（美国空军人工智能加速器）
Looping in the Human Collaborative and Explainable Bayesian Optimization
University of Oxford（牛津大学）；Toyota Motor Corporation（丰田汽车公司）；The Faraday Institution（法拉第研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
