The development of novel technologies has increasingly complicated the issue of users' privacy. This study aimed to investigate the impact that the types of information as well as agents have on perceptions of privacy risks and concerns. The results showed that participants perceived privacy risk and were most concerned when they were asked to provide digital life information, followed by being asked for digital footprint information, and even more so when they were asked to provide this information to a human agent instead of an artificial intelligence agent. However, there were no differences in the perception of privacy risks and concerns with respect to self-expression and demographic information. The implications of these findings are also discussed.
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http://dx.doi.org/10.1089/cyber.2021.0076 | DOI Listing |
JMIR AI
March 2025
Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany.
Background: Physician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy.
View Article and Find Full Text PDFJ Med Internet Res
March 2025
College of Nursing, Michigan State University, East Lansing, MI, United States.
Background: The increasing number of older adults who are living alone poses challenges for maintaining their well-being, as they often need support with daily tasks, health care services, and social connections. However, advancements in artificial intelligence (AI) technologies have revolutionized health care and caregiving through their capacity to monitor health, provide medication and appointment reminders, and provide companionship to older adults. Nevertheless, the adaptability of these technologies for older adults is stymied by usability issues.
View Article and Find Full Text PDFJ Agric Saf Health
October 2024
Department of Biological and Agricultural Engineering, University of California, Davis, California, USA.
Highlights: An AI-driven system for predicting and preventing ATV crashes was developed. Machine learning model achieved rollover prediction accuracy of over 99%. The system has the potential to significantly reduce ATV-related injuries and fatalities by enabling preemptive actions.
View Article and Find Full Text PDFJ Am Med Inform Assoc
March 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Objectives: While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.
Materials And Methods: We explore fairness drift in a national population over 11 years, with and without model maintenance aimed at sustaining population-level performance.
J Acoust Soc Am
March 2025
Department of Information and Communications Engineering, Aalto University, Espoo 02150, Finland.
Vocal intensity is quantified by sound pressure level (SPL). The SPL can be measured by either using a sound level meter or by comparing the energy of the recorded speech signal with the energy of the recorded calibration tone of a known SPL. Neither of these approaches can be used if speech is recorded in real-life conditions using a device that is not calibrated for SPL measurements.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!