Contextual Trust
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Abstract (arXiv)
Trust is an important aspect of human life. It provides instrumental value in allowing us to collaborate on and defer actions to others, and intrinsic value in our intimate relationships with romantic partners, family, and friends. In this paper I examine the nature of trust from a philosophical perspective. Specifically I propose to view trust as a context-sensitive state in a manner that will be made precise. The contribution of this paper is threefold. First, I make the simple observation that an individual's trust is typically both action- and context-sensitive. Action-sensitivity means that trust may obtain between a given truster and trustee for only certain actions. Context-sensitivity means that trust may obtain between a given truster and trustee, regarding the same action, in some conditions and not others. I also opine about what kinds of things may play the role of the truster, trustee, and action. Second, I advance a theory for the nature of contextual trust. I propose that the answer to "What does it mean for A to trust B to do X in context C?" has two conditions. First, A must take B's doing X as a means towards one of A's ends. Second, A must adopt an unquestioning attitude concerning B's doing X in context C. This unquestioning attitude is similar to the attitude introduced in Nguyen 2021. Finally, we explore how contextual trust can help us make sense of trust in general non-interpersonal settings, notably that of artificial intelligence (AI) systems. The field of Explainable Artificial Intelligence (XAI) assigns paramount importance to the problem of user trust in opaque computational models, yet does little to give trust diagnostic or even conceptual criteria. I propose that contextual trust is a natural fit for the task by illustrating that model transparency and explainability map nicely into our construction of the contexts C.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 51 references that are in the map. First posted on arXiv 15 Mar 2023.
Builds on
9 of 51 references.
References · 9
- 2017Attention Is All You Needcited by 60,361
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
- 2016The Mythos of Model Interpretabilitycited by 721
- 2016Generating Visual Explanationscited by 158
- 2019How model accuracy and explanation fidelity influence user trustcited by 45
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Citing papers per year: 2024: 1.
1 paper.