Designing for Responsible Trust in AI Systems: A Communication Perspective

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.

Design, Development, and Deployment of Context-Adaptive AI Systems for Enhanced User Adoption

“…Similarly, consideration of situational factors of past interactions with others in the design of human-centered AI systems has been emphasized [10]. Understanding these human-centered factors is crucial for enhancing user trust and adoption, and ensuring successful integration of AI systems [11,26,27].…”

Modeling Human Trust and Reliance in AI-Assisted Decision Making: A Markovian Approach

“…Researchers have identified a large set of factors that may influence people's reliance on AI, including the AI model's accuracy (Yin, Wortman Vaughan, and Wallach 2019;Lai and Tan 2019), confidence (Zhang, Liao, and Bellamy 2020;Rechkemmer and Yin 2022), the type of AI explanations and the ways that they are presented (Yang et al 2020;Bansal et al 2021b), humans' mental models about AI (Bansal et al 2019a,b), and the level of humanmodel agreement (Lu and Yin 2021). It was shown in many experimental studies that decision makers often can not rely on AI models appropriately, which leads to new studies on designing innovative methods to promote appropriate reliance on AI (Buçinca, Malaya, and Gajos 2021;Park et al 2019;Liao and Sundar 2022;Chiang and Yin 2022).…”