AIMC Topic: Trust

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Assessing supervisor versus trainee viewpoints of entrustment through cognitive and affective lenses: an artificial intelligence investigation of bias in feedback.

Advances in health sciences education : theory and practice
The entrustment framework redirects assessment from considering only trainees' competence to decision-making about their readiness to perform clinical tasks independently. Since trainees and supervisors both contribute to entrustment decisions, we ex...

The role of trust in the use of artificial intelligence for chemical risk assessment.

Regulatory toxicology and pharmacology : RTP
Risk assessment of chemicals is a time-consuming process and needs to be optimized to ensure all chemicals are timely evaluated and regulated. This transition could be stimulated by valuable applications of in silico Artificial Intelligence (AI)/Mach...

A Turing test of whether AI chatbots are behaviorally similar to humans.

Proceedings of the National Academy of Sciences of the United States of America
We administer a Turing test to AI chatbots. We examine how chatbots behave in a suite of classic behavioral games that are designed to elicit characteristics such as trust, fairness, risk-aversion, cooperation, etc., as well as how they respond to a ...

Unlocking human-robot synergy: The power of intent communication in warehouse robotics.

Applied ergonomics
As autonomous mobile robots (AMR) are introduced into workspace environments shared with people, effective human-robot communication is critical to the prevention of injury while maintaining a high level of productivity. This research presents an emp...

Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics.

Public understanding of science (Bristol, England)
The governance of artificial intelligence (AI) is an urgent challenge that requires actions from three interdependent stakeholders: individual citizens, technology corporations, and governments. We conducted an online survey ( = 525) of US adults to ...

Data-driven approach to quantify trust in medical devices using Bayesian networks.

Experimental biology and medicine (Maywood, N.J.)
Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our...

Robots for surgeons? Surgeons for robots? Exploring the acceptance of robotic surgery in the light of attitudes and trust in robots.

BMC psychology
BACKGROUND: Over the last century, technological progress has been tremendous, and technological advancement is reflected in the development of medicine. This research assessed attitudes towards surgical robots and identified correlations with willin...

Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists.

The Laryngoscope
BACKGROUND: Machine learning driven clinical decision support tools (ML-CDST) are on the verge of being integrated into clinical settings, including in Otolaryngology-Head & Neck Surgery. In this study, we investigated whether such CDST may influence...

Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study.

JMIR human factors
BACKGROUND: Artificial intelligence (AI)-powered technologies are being increasingly used in almost all fields, including medicine. However, to successfully implement medical AI applications, ensuring trust and acceptance toward such technologies is ...