Who to Trust, How and Why: Untangling AI Ethics Principles, Trustworthiness and Trust

TL;DR

This paper reviews trust in AI, emphasizing the distinction between trust and trustworthiness, and highlights the need for empirical research on how AI features influence user trust within socio-technical contexts.

Contribution

It clarifies key concepts of trust and trustworthiness in AI, advocating for empirical studies and a socio-technical perspective to better understand trust dynamics.

Findings

Trust involves reliance on AI and its developers.

Empirical evidence on explainability's impact on trust is limited.

Recognizing socio-technical factors is crucial for trustworthy AI.

Abstract

We present an overview of the literature on trust in AI and AI trustworthiness and argue for the need to distinguish these concepts more clearly and to gather more empirically evidence on what contributes to people s trusting behaviours. We discuss that trust in AI involves not only reliance on the system itself, but also trust in the developers of the AI system. AI ethics principles such as explainability and transparency are often assumed to promote user trust, but empirical evidence of how such features actually affect how users perceive the system s trustworthiness is not as abundance or not that clear. AI systems should be recognised as socio-technical systems, where the people involved in designing, developing, deploying, and using the system are as important as the system for determining whether it is trustworthy. Without recognising these nuances, trust in AI and trustworthy AI risk becoming nebulous terms for any desirable feature for AI systems.