AIMC Topic: Trust

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How to achieve trustworthy artificial intelligence for health.

Bulletin of the World Health Organization
Artificial intelligence holds great promise in terms of beneficial, accurate and effective preventive and curative interventions. At the same time, there is also awareness of potential risks and harm that may be caused by unregulated developments of ...

Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare.

Bulletin of the World Health Organization
Empathy, compassion and trust are fundamental values of a patient-centred, relational model of health care. In recent years, the quest for greater efficiency in health care, including economic efficiency, has often resulted in the side-lining of thes...

Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach.

Applied ergonomics
The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automat...

The black sheep effect: The case of the deviant ingroup robot.

PloS one
The black sheep effect (BSE) describes the evaluative upgrading of norm-compliant group members (ingroup bias), and evaluative downgrading of deviant (norm-violating) group members, relative to similar outgroup members. While the BSE has been demonst...

Trust in socially assistive robots: Considerations for use in rehabilitation.

Neuroscience and biobehavioral reviews
Incorporation of social robots into rehabilitation calls for understanding what factors affect user motivation and success of the interaction. Trust between the user and the robot has been identified as important in human-robot interaction and in hum...

A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?

Systematic reviews
BACKGROUND: Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools.

The Relationship Between Trust and Use Choice in Human-Robot Interaction.

Human factors
OBJECTIVE: To understand the influence of trust on use choice in human-robot interaction via experimental investigation.

Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction.

Human factors
OBJECTIVE: We investigated the co-acting influences of communication and social conformity on trust in human-robot interaction.

Machine learning in medicine: Addressing ethical challenges.

PLoS medicine
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians.