Article
Computer Science, Cybernetics
Kristin Weger, Lisa Matsuyama, Rileigh Zimmermann, Bryan Mesmer, Douglas Van Bossuyt, Robert Semmens, Casey Eaton
Summary: Findings reveal that ease of use and system reliability are significant factors for the acceptance and adoption of autonomous systems, regardless of the level of automation. Understanding the critical factors of human-automation interaction in mission critical environments can enable seamless acceptance and adoption of autonomous systems, resulting in more effective and cost-efficient mission execution.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Multidisciplinary Sciences
Tobias Rieger, Eileen Roesler, Dietrich Manzey
Summary: This study investigates the interaction between humans and technological support systems in terms of trust and responsibility through three experiments. The findings show that from a collaborative perspective, human support is preferred over technical support systems in terms of trust and responsibility. However, from an assessed perspective, people tend to prefer technical systems.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Husam Muslim, Makoto Itoh, Cho Kiu Liang, Jacobo Antona-Makoshi, Nobuyuki Uchida
Summary: This study compared four automated driving systems (ADS) designs in lane change demanding traffic situations, considering drivers' demographic factors. The results showed that the impact of demographic factors on ADS designs increases when the systems appear more driver-like. Understanding the interaction between human demographic factors and system design can help predict ADS users' behavior.
SCIENTIFIC REPORTS
(2021)
Article
Biochemistry & Molecular Biology
Katharine E. Henry, Roy Adams, Cassandra Parent, Hossein Soleimani, Anirudh Sridharan, Lauren Johnson, David N. Hager, Sara E. Cosgrove, Andrew Markowski, Eili Y. Klein, Edward S. Chen, Mustapha O. Saheed, Maureen Henley, Sheila Miranda, Katrina Houston, Robert C. Linton, Anushree R. Ahluwalia, Albert W. Wu, Suchi Saria
Summary: A prospective evaluation of a machine learning-based early warning system for sepsis showed that healthcare providers interacted with the system at a high rate, resulting in faster antibiotic ordering. The early detection tool achieved high sensitivity and adoption rates, with confirmed alerts leading to a reduction in median time to antibiotic order. Emergency department providers and those with previous interactions with the alert were more likely to interact and confirm alerts.
Article
Computer Science, Cybernetics
Ryosuke Yokoi, Kazuya Nakayachi
Summary: This study investigates the impact of moral value similarity on trust in automation systems in transportation and medical care fields. The results indicate that trust in the systems is enhanced when their values are similar to those of the participants. However, automation systems were trusted less than human agents in both experiments.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2021)
Article
Psychology, Multidisciplinary
Lori Dithurbide, Heather F. Neyedli, Jamie Swinimer, Jamie MacFarlane
Summary: The decision of athletes to use technology is based on trust in automation and confidence in their abilities. Studies found that giving a DMD to non-users influenced trust in automation and confidence, while DMD users experienced a brief decrease in confidence when relinquishing the device but quickly rebounded. Performance remained unchanged in both groups.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Transportation Science & Technology
Hebert Azevedo-Sa, Huajing Zhao, Connor Esterwood, X. Jessie Yang, Dawn M. Tilbury, Lionel P. Robert
Summary: The study found that high reliability in automated driving systems increases trust and further enhances the performance of non-driving related tasks, while low visibility reduces the negative impact of trust on driver monitoring. Trust in the system increases over time if it is reliable, and visibility does not have a significant impact on trust. These findings are crucial for designing intelligent automated driving systems that can adapt to drivers' trusting behaviors.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Psychology, Applied
Jieun Lee, Genya Abe, Kenji Sato, Makoto Itoh
Summary: This study manipulated prior information and automation failures to investigate the development of driver trust in automation. The results showed that trust steadily increased with simulation experience, decreased temporarily with automation failure, and was rebuilt with subsequent flawless experiences. Dependability was found to be the most dominant belief influencing driver trust.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2021)
Article
Computer Science, Cybernetics
Jean-Baptiste Manchon, Romane Beaufort, Mercedes Bueno, Jordan Navarro
Summary: As the development of driving automation accelerates, the relationship between humans and automated driving is evolving accordingly. Trust in automated driving is strongly related to drivers' acceptance of such systems. This article aims to propose a new scale to evaluate initial trust in automated driving and explore the factors influencing drivers' initial trust in Highly Automated Driving (HAD) systems. The collected data is used to build a linear model that not only weighs the factors determining trust but also predicts the level of trust considering these factors.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Psychology, Applied
N. Neuhuber, P. Pretto, B. Kubicek
Summary: This study aims to explore the interaction strategies of different drivers when using advanced driver assistance systems and address the unsafe usage through categorization. The results suggest the need for tailored tutoring systems for different driver categories.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2022)
Article
Information Science & Library Science
Eva-Maria Schomakers, Hannah Biermann, Martina Ziefle
Summary: Smart home automation offers convenience in daily life but faces user concerns about privacy and trust. A study found that semi-automated systems are preferred over fully automated ones, with reliability of automation being the key determinant of acceptance. Awareness, data type, and application field are less important factors for user willingness to use smart homes.
TELEMATICS AND INFORMATICS
(2021)
Article
Engineering, Industrial
William H. Sharp, Kenneth M. Jackson, Tyler H. Shaw
Summary: The study aimed to investigate the impact of positive and negative interaction frequency with a computer agent on trust resilience after a major error, and empirically test the concept of relationship equity on user trust in a transfer task. Participants were randomly assigned to four groups with different interaction frequencies and positivity levels, and completed pattern recognition and transfer tasks. Data on subjective trust ratings, performance, compliance, and agreement were collected and analyzed. Results showed that the frequency of positive and negative interactions affected user trust and trust resilience, and relationship equity also influenced trust and trust resilience. This is the first empirical demonstration of the impact of relationship equity on user trust in an automated teammate.
APPLIED ERGONOMICS
(2023)
Article
Psychology, Multidisciplinary
Francesco Walker, Yannick Forster, Sebastian Hergeth, Johannes Kraus, William Payre, Philipp Wintersberger, Marieke Martens
Summary: This paper explores the conceptualization, calibration, and measurement of trust in driving automation systems. The study finds that experience is crucial for trust calibration and should be measured based on the range of encountered situations. Additionally, system malfunctions and recovery play a fundamental role in this experience. However, current methods used to quantify trust lack objectivity, reliability, and validity. The safe deployment of automated vehicles relies on drivers developing appropriate levels of trust and incorporating the possibility of new and unexpected driving situations into their mental models of system capabilities.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Computer Science, Cybernetics
Lindsay Sanneman, Julie A. Shah
Summary: Recent advances in AI have highlighted the importance of AI systems being understandable to humans. The XAI literature aims to improve human understanding and performance by providing users with information about AI system behavior. Drawing from the human factors literature, the authors propose a three-level framework for the development and evaluation of AI explanations based on the informational needs of human users. The framework also includes considerations for workload and trust, along with methods for assessing the effectiveness of XAI systems.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2022)
Article
Computer Science, Cybernetics
Melissa Saragih, Ben W. Morrison
Summary: The study found that as past accuracy and decision significance increased, people were more likely to accept algorithmic advice. In high-stakes scenarios, people were more likely to accept algorithmic advice that far exceeded previous performance.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2022)
Article
Computer Science, Artificial Intelligence
Larry Bunch, Jeffrey M. Bradshaw, Robert R. Hoffman, Matthew Johnson
IEEE INTELLIGENT SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
Matthew Johnson, Jeffrey M. Bradshaw, Paul J. Feltovich, Catholijn Jonker, Birna van Riemsdijk, Maarten Sierhuis
IEEE INTELLIGENT SYSTEMS
(2012)
Editorial Material
Computer Science, Artificial Intelligence
Jeffrey M. Bradshaw, Virginia Dignum, Catholijn Jonker, Maarten Sierhuis
IEEE INTELLIGENT SYSTEMS
(2012)
Article
Computer Science, Artificial Intelligence
Jeffrey M. Bradshaw, Robert R. Hoffman, Matthew Johnson, David D. Woods
IEEE INTELLIGENT SYSTEMS
(2013)
Article
Computer Science, Artificial Intelligence
Niranjan Suri, Andrzej Uszok, Rita Lenzi, Maggie Breedy, Jeffrey M. Bradshaw, Yat Fu, James Hanna, Vaughn T. Combs, Asher Sinclair, Robert Grant
IEEE INTELLIGENT SYSTEMS
(2013)
Article
Computer Science, Artificial Intelligence
Andrzej Uszok, Larry Bunch, Jeffrey M. Bradshaw, Thomas Reichherzer, James Hanna, Albert Frantz
IEEE INTELLIGENT SYSTEMS
(2013)
Editorial Material
Computer Science, Artificial Intelligence
Robert R. Hoffman, John K. Hawley, Jeffrey M. Bradshaw
IEEE INTELLIGENT SYSTEMS
(2014)
Article
Computer Science, Artificial Intelligence
Matthew Johnson, Jeffrey M. Bradshaw, Robert R. Hoffman, Paul J. Feltovich, David D. Woods
IEEE INTELLIGENT SYSTEMS
(2014)
Article
Computer Science, Cybernetics
Jeffrey M. Bradshaw
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
(2013)
Proceedings Paper
Computer Science, Artificial Intelligence
Enrico Casini, Jessica Depree, Niranjan Suri, Jeffrey M. Bradshaw, Teresa Nieten
2015 IEEE INTERNATIONAL MULTI-DISCIPLINARY CONFERENCE ON COGNITIVE METHODS IN SITUATION AWARENESS AND DECISION SUPPORT (COGSIMA)
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
Enrico Casini, Niranjan Suri, Jeffrey M. Bradshaw
NEXT-GENERATION ANALYST III
(2015)
Article
Humanities, Multidisciplinary
Jeffrey M. Bradshaw
BYU STUDIES QUARTERLY
(2014)
Proceedings Paper
Automation & Control Systems
Marco Carvalho, Thomas C. Eskridge, Larry Bunch, Adam Dalton, Robert Hoffman, Daniel Kidwell, Teresa Shanklin
2013 6TH INTERNATIONAL SYMPOSIUM ON RESILIENT CONTROL SYSTEMS (ISRCS)
(2013)
Article
Computer Science, Artificial Intelligence
Jeffrey M. Bradshaw, Marco Carvalho, Larry Bunch, Tom Eskridge, Paul J. Feltovich, Matt Johnson, Dan Kidwell
KUNSTLICHE INTELLIGENZ
(2012)
