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

arXiv: https://arxiv.org/abs/2304.08795 · HTML: https://mlsanity.com/paper/2304.08795/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 111 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://mlsanity.com/paper/1911.06473/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash](https://mlsanity.com/paper/1805.11815/) (2018)
- [Mechanism Design for Social Good](https://mlsanity.com/paper/1810.09832/) (2018)
- [In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes](https://mlsanity.com/paper/1912.02675/) (2019)
- [Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development](https://mlsanity.com/paper/1910.03515/) (2019)

## Built on by (most cited first, 30 of 33)

- [Principles for Responsible AI Consciousness Research](https://mlsanity.com/paper/2501.07290/) (2025)
- [Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistance](https://mlsanity.com/paper/2502.13321/) (2025)
- [May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability](https://mlsanity.com/paper/2309.13965/) (2023)
- [An Empirical Exploration of Trust Dynamics in LLM Supply Chains](https://mlsanity.com/paper/2405.16310/) (2024)
- [Establishing Trustworthiness: Rethinking Tasks and Model Evaluation](https://mlsanity.com/paper/2310.05442/) (2023)
- [Understanding Human-AI Trust in Education](https://mlsanity.com/paper/2506.09160/) (2025)
- [Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review](https://mlsanity.com/paper/2310.03392/) (2023)
- [Fool Me, Fool Me: User Attitudes Toward LLM Falsehoods](https://mlsanity.com/paper/2412.11625/) (2024)
- ["That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation](https://mlsanity.com/paper/2505.22845/) (2025)
- [Human-centered trust framework: An HCI perspective](https://mlsanity.com/paper/2305.03306/) (2023)
- [Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scale](https://mlsanity.com/paper/2306.05036/) (2023)
- [Who to Trust, How and Why: Untangling AI Ethics Principles, Trustworthiness and Trust](https://mlsanity.com/paper/2309.10318/) (2023)
- [HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents](https://mlsanity.com/paper/2403.02752/) (2024)
- [Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired](https://mlsanity.com/paper/2508.03651/) (2025)
- [Challenges and Trends in User Trust Discourse in AI](https://mlsanity.com/paper/2305.11876/) (2023)
- [The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-sectional Survey Analysis](https://mlsanity.com/paper/2311.05632/) (2023)
- [Conversational Explanations: Discussing Explainable AI with Non-AI Experts](https://mlsanity.com/paper/2503.16444/) (2025)
- [AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support](https://mlsanity.com/paper/2602.15865/) (2026)
- [From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants](https://mlsanity.com/paper/2604.05166/) (2026)
- [VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication](https://mlsanity.com/paper/2503.07279/) (2025)
- [Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology](https://mlsanity.com/paper/2504.04833/) (2025)
- [Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces](https://mlsanity.com/paper/2504.20782/) (2025)
- [Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations](https://mlsanity.com/paper/2507.21089/) (2025)
- [From keywords to semantics: Perceptions of large language models in data discovery](https://mlsanity.com/paper/2510.01473/) (2025)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)
- [In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions](https://mlsanity.com/paper/2510.16173/) (2025)
- [The Personalization Paradox: Semantic Loss vs. Reasoning Gains in Agentic AI Q&A](https://mlsanity.com/paper/2512.04343/) (2025)
- [When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption](https://mlsanity.com/paper/2601.21650/) (2026)
- [Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency](https://mlsanity.com/paper/2602.07754/) (2026)
- [From Performance to Purpose: A Sociotechnical Taxonomy for Evaluating Large Language Model Utility](https://mlsanity.com/paper/2602.20513/) (2026)

## Abstract (arXiv)

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.