AIMC Topic: Trust

Clear Filters Showing 161 to 170 of 257 articles

Trust in Artificial Intelligence: Meta-Analytic Findings.

Human factors
OBJECTIVE: The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of...

Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study.

International journal of environmental research and public health
A diagnostic decision support system (DDSS) is expected to reduce diagnostic errors. However, its effect on physicians' diagnostic decisions remains unclear. Our study aimed to assess the prevalence of diagnoses from artificial intelligence (AI) in p...

Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?

Journal of medical ethics
We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel med...

Promises and trust in human-robot interaction.

Scientific reports
Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and contexts. The aim of this paper is to investigate whether human-beings trust a social robot-i.e. a ...

Trust in artificial intelligence within production management - an exploration of antecedents.

Ergonomics
Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI...

Coming to Terms with the Black Box Problem: How to Justify AI Systems in Health Care.

The Hastings Center report
The use of opaque, uninterpretable artificial intelligence systems in health care can be medically beneficial, but it is often viewed as potentially morally problematic on account of this opacity-because the systems are black boxes. Alex John London ...

The effect of facial features on facial anthropomorphic trustworthiness in social robots.

Applied ergonomics
As the nature of human-robot relationships have become increasingly bound to shift from supervisor-machine to friend-companion, people have exhibited an increasing interest in making social judgments toward such anthropomorphic objects, such as trust...

The use of personal health information outside the circle of care: consent preferences of patients from an academic health care institution.

BMC medical ethics
BACKGROUND: Immense volumes of personal health information (PHI) are required to realize the anticipated benefits of artificial intelligence in clinical medicine. To maintain public trust in medical research, consent policies must evolve to reflect c...

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis.

PloS one
In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, partic...