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Related Concept Videos

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Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
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When in group settings, we are often influenced by the thoughts, feelings, and behaviors around us. Groupthink is another phenomenon of conformity where modification of the opinions of members in a group aligns with what they believe is the group consensus (Janis, 1972). In such situations, the group often takes action that individuals would not perform outside the group setting because groups make more extreme decisions than individuals do. Moreover, groupthink can hinder opposing trains of...
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Related Experiment Video

Updated: Apr 16, 2026

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

Published on: October 20, 2022

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Trust Is the Bottleneck.

Heena Rathore

    IEEE Pulse
    |April 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Building trust in artificial intelligence (AI) for healthcare requires more than just accuracy. Ensuring transparency, accountability, and interpretability is crucial for clinical adoption of medical AI systems.

    Related Experiment Videos

    Last Updated: Apr 16, 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

    2.7K

    Area of Science:

    • Biomedical Engineering
    • Health Informatics
    • Artificial Intelligence in Medicine

    Background:

    • Artificial intelligence (AI) is rapidly advancing in healthcare, impacting areas like diagnostic imaging and personalized medicine.
    • Despite technical progress, a lack of trust remains a significant barrier to the widespread clinical adoption of AI systems.
    • The second IEEE EMBS Workshop on AI and Healthcare convened to address the multifaceted nature of trust in medical AI.

    Purpose of the Study:

    • To synthesize insights on the multidimensional aspects of trust in medical AI systems.
    • To explore the factors influencing clinical and public confidence in AI applications within healthcare.
    • To identify key challenges and requirements for fostering trustworthy AI in clinical settings.

    Main Methods:

    • Synthesis of discussions from the IEEE EMBS Workshop on AI and Healthcare.
    • Analysis of keynote demonstrations featuring AI-enabled ultrasound.
    • Incorporation of insights from an interdisciplinary panel and audience polling/case-based discussions.

    Main Results:

    • Trust in medical AI is contingent on accuracy, transparency, accountability, and interpretability.
    • Clinical and public confidence in AI is conditional and context-dependent, especially in high-stakes scenarios.
    • Key tensions exist between AI performance and explainability, necessitating accountability frameworks and lifecycle oversight.

    Conclusions:

    • Trustworthy healthcare AI necessitates robust validation, human-centered design, and interdisciplinary collaboration.
    • Sustained governance is essential for AI to transition from a promising technology to a reliable clinical partner.
    • The successful integration of AI in healthcare hinges on its ability to earn and maintain trust, not solely on algorithmic capabilities.