Designing for Responsible Trust in AI Systems: A Communication Perspective

TL;DR

This paper introduces the MATCH model, a communication-based framework for responsibly conveying AI trustworthiness to users, emphasizing transparency, interaction, and the importance of selecting appropriate trust cues to foster equitable trust.

Contribution

It develops the MATCH conceptual model, integrating communication theories to guide AI designers in responsibly communicating trustworthiness and preventing deception.

Findings

MATCH highlights trustworthiness cues like transparency and interaction.

It introduces concepts of warranted and expensive trust cues.

Provides a checklist for selecting appropriate trust cues.

Abstract

Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users' cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.