Human and AI Trust: Trust Attitude Measurement Instrument

TL;DR

This paper develops and validates a 16-item trust measurement instrument specifically designed to assess laypeople's trust attitudes towards AI systems, especially in medical support contexts.

Contribution

It introduces a psychometrically sound trust scale tailored for human-AI interaction research, validated through rigorous testing.

Findings

The instrument is reliable and valid for measuring trust in AI medical support systems.

The scale effectively differentiates trust levels among non-experts.

The development process ensures the instrument's applicability in diverse human-AI interaction studies.

Abstract

With the current progress of Artificial Intelligence (AI) technology and its increasingly broader applications, trust is seen as a required criterion for AI usage, acceptance, and deployment. A robust measurement instrument is essential to correctly evaluate trust from a human-centered perspective. This paper describes the development and validation process of a trust measure instrument, which follows psychometric principles, and consists of a 16-items trust scale. The instrument was built explicitly for research in human-AI interaction to measure trust attitudes towards AI systems from layperson (non-expert) perspective. The use-case we used to develop the scale was in the context of AI medical support systems (specifically cancer/health prediction). The scale development (Measurement Item Development) and validation (Measurement Item Evaluation) involved six research stages: item development, item evaluation, survey administration, test of dimensionality, test of reliability, and test of validity. The results of the six-stages evaluation show that the proposed trust measurement instrument is empirically reliable and valid for systematically measuring and comparing non-experts' trust in AI Medical Support Systems.