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<p>This manuscript explores the evolving concept of trust in human-AI relationships through two empirical studies conducted in complementary domains. As artificial intelligence (AI) becomes increasingly integrated into high-stakes decision-making domains such as healthcare and employment, we examine how trust manifests along two key dimensions: functional (related to capability and performance) and relational (related to social desirability and sincerity). Study 1, based on previously published analyses (Oh &amp; Jung,&#xa0;<CitationRef CitationID="CR31">2023</CitationRef>), examines public perceptions of AI's technological capability and social desirability in replacing human workers across 100 occupations, drawing on responses from a U.S.-based sample. Study 2 explores how different types of diagnostic uncertainty, epistemic and aleatory, communicated by medical AI chatbots affect perceived credibility and sincerity. Together, the findings underscore that trust in AI is deeply context-dependent, influenced by perceived competence, communication style, and user expectations. The manuscript contributes to understanding trust as involving both functional and relational dimensions in human–AI interaction, with implications for AI system design and user engagement.</p>

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Rethinking trust in AI: empirical insights from labor and healthcare domains

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Younbo Jung,
  • Poong Oh,
  • Jiajing Zhai

摘要

This manuscript explores the evolving concept of trust in human-AI relationships through two empirical studies conducted in complementary domains. As artificial intelligence (AI) becomes increasingly integrated into high-stakes decision-making domains such as healthcare and employment, we examine how trust manifests along two key dimensions: functional (related to capability and performance) and relational (related to social desirability and sincerity). Study 1, based on previously published analyses (Oh & Jung, 2023), examines public perceptions of AI's technological capability and social desirability in replacing human workers across 100 occupations, drawing on responses from a U.S.-based sample. Study 2 explores how different types of diagnostic uncertainty, epistemic and aleatory, communicated by medical AI chatbots affect perceived credibility and sincerity. Together, the findings underscore that trust in AI is deeply context-dependent, influenced by perceived competence, communication style, and user expectations. The manuscript contributes to understanding trust as involving both functional and relational dimensions in human–AI interaction, with implications for AI system design and user engagement.