Contextual Trust

Trust is action-specific and context-dependent: The paper demonstrates that we don't trust someone universally, but rather for particular actions in particular situations. For AI systems, this means we should evaluate trustworthiness not as a blanket quality, but by asking "Can I trust this AI to do X in situation Y?"—a framework that could help organizations and users develop more realistic expectations of AI capabilities.

Trust involves both instrumental reliance and an "unquestioning attitude": According to the paper's theory, trust requires two elements: (1) believing someone's actions will help achieve your goals, and (2) adopting an attitude of not constantly second-guessing their decisions. This distinction is crucial for responsible AI deployment, suggesting that appropriate trust in AI systems should include built-in verification mechanisms rather than blind reliance.

The framework extends beyond human relationships to artificial systems: The paper explicitly explores how contextual trust applies to "non-interpersonal settings, notably that of artificial intelligence," suggesting that the same philosophical principles governing human trust can be meaningfully applied to AI. This provides a structured way to think about when and how AI systems should be trusted in different applications.

Context matters as much as capability: By emphasizing that the same action between the same parties can be trustworthy in one context but not another, the paper highlights that responsible AI governance must account for situational factors—not just whether an AI system works well in general, but whether it's appropriate for specific deployment contexts.