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Related Experiment Video

Updated: Jul 14, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

'Trust Is the First Algorithm': A Socio-Technical Framework for Generative AI Integration in a High-Stakes,

Ibrahim Aqtam1, Mustafa Shouli1

  • 1Ibn Sina College for Health Professions, Nablus University for Vocational and Technical Education, Nablus, West Bank, Palestine.

Journal of Advanced Nursing
|July 13, 2026
PubMed
Summary

Developing trust in artificial intelligence (AI) requires explainability, risk-benefit analysis, contextual intelligence, and co-design. Nurses need involvement in AI design and governance for safe implementation in healthcare.

Keywords:
Palestinegenerative AIimplementationprofessional autonomyqualitative researchresource‐constrained settingsscience and technology studiessocio‐technical systemstrust

Related Experiment Videos

Last Updated: Jul 14, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

Area of Science:

  • Healthcare technology
  • Artificial intelligence ethics
  • Socio-technical systems

Background:

  • Trust in artificial intelligence (AI) is crucial, especially in high-stakes, resource-constrained healthcare settings.
  • Understanding the nuances of trust is vital for the effective integration of AI into clinical practice.

Purpose of the Study:

  • To develop a conceptual framework for understanding trust in AI within resource-limited environments.
  • To explore the perspectives of nurses in Palestine regarding AI trust as a critical case study.

Main Methods:

  • A qualitative descriptive study involving 25 nurses was conducted.
  • Face-to-face interviews were analyzed using reflexive thematic analysis.
  • Rigorous translation and credibility strategies were employed to ensure findings' validity.

Main Results:

  • Four key themes emerged: Explainable Trust, Contextual Intelligence, Algorithmic Ignorance, and Co-Design.
  • Participant narratives enriched the conceptual framework with context-specific subthemes like 'override mandate'.
  • The framework was iteratively refined, integrating local context into AI trust principles.

Conclusions:

  • Trust in AI is a socio-technical precondition, requiring active engagement.
  • The developed framework offers transferable principles for AI trust in diverse, unequal, high-stakes contexts.
  • Nurses' involvement in AI design, training, and governance, including override authority, is essential.