A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective

Abstract

User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption. It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human centric approach, a core principle of the human-computer interaction (HCI) field. This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies to gather insight for future technical and design strategies, research, and initiatives to calibrate the user AI relationship. The findings confirm that there is more than one way to define trust. Selecting the most appropriate trust definition to depict user trust in a specific context should be the focus instead of comparing definitions. User trust in AI-enabled systems is found to be influenced by three main themes, namely socio-ethical considerations, technical and design features, and user characteristics. User characteristics dominate the findings, reinforcing the importance of user involvement from development through to monitoring of AI enabled systems. In conclusion, user trust needs to be addressed directly in every context where AI-enabled systems are being used or discussed. In addition, calibrating the user-AI relationship requires finding the optimal balance that works for not only the user but also the system.

References in corpus (5)

• Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
• In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes
• Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
• "How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
• Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development

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• Challenges and Trends in User Trust Discourse in AI
• CUI@CHI 2024: Building Trust in CUIs-From Design to Deployment
• Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development
• VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication
• HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents
• A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work