How Can I Signal You To Trust Me: Investigating AI Trust Signalling in Clinical Self-Assessments

Abstract

Individuals are increasingly interested in and responsible for assessing their own health. This study evaluates a fictional AI dermatologist for assistance in the self-assessment of moles. Building on the Signalling Theory, we tested the effect of textual descriptions provided by a virtual dermatologist, as manipulated across ‘Ability’, ‘Integrity, ’ and ‘Benevolence’, along with the clinical assessment, ‘benign’ or ‘malignant’, affect users’ trust in the aforementioned trust pillars. Our study (N = 40) follows a 2 (Ability low/high) × 2 (Integrity low/high) × 2 (Benevolence low/high) × 2 (mole assessment benign/malignant) within-subject factorial design. Our results demonstrate that we can successfully influence perceptions of ability and benevolence by manipulating the corresponding aspects of trust but not perceived integrity. Further, in the case of a malignant assessment, participants’ perception of trust increased across all aspects. Our results provide insights into the design of AI support systems for sensitive use cases, such as clinical self-assessments.

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