mlsanityData to 7 Oct 2026

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

arXiv 2304.08795 ↗First posted 18 Apr 2023cs.HC cs.AI cs.CYcited by 33

Not on the map. Why?

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.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 11 of its 111 references that are in the map. First posted on arXiv 18 Apr 2023.

Builds on

11 of 111 references.

References · 11

  1. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  2. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  3. 2019Principles alone cannot guarantee ethical AIcited by 134
  4. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  5. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  6. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  7. 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
  8. 2018Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crashcited by 13
  9. 2018Mechanism Design for Social Goodcited by 13
  10. 2019In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processescited by 13
  11. 2019Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Developmentcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 3; 2025: 14; 2026: 8.

33 papers.

Sort:

By area:

  1. 2025Principles for Responsible AI Consciousness Researchcited by 6
  2. 2025Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistancecited by 6
  3. 2023May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainabilitycited by 5
  4. 2024An Empirical Exploration of Trust Dynamics in LLM Supply Chainscited by 5
  5. 2023Establishing Trustworthiness: Rethinking Tasks and Model Evaluationcited by 4
  6. 2025Understanding Human-AI Trust in Educationcited by 4
  7. 2023Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Reviewcited by 3
  8. 2024Fool Me, Fool Me: User Attitudes Toward LLM Falsehoodscited by 3
  9. 2023Human-centered trust framework: An HCI perspectivecited by 2
  10. 2023Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scalecited by 2
  11. 2023Who to Trust, How and Why: Untangling AI Ethics Principles, Trustworthiness and Trustcited by 2
  12. 2024HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agentscited by 2
21 more
  1. 2025"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformationcited by 2
  2. 2025Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impairedcited by 2
  3. 2023Challenges and Trends in User Trust Discourse in AIcited by 1
  4. 2023The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-sectional Survey Analysiscited by 1
  5. 2025Conversational Explanations: Discussing Explainable AI with Non-AI Expertscited by 1
  6. 2026AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Supportcited by 1
  7. 2026From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistantscited by 1
  8. 2025VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communicationcited by 0
  9. 2025Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytologycited by 0
  10. 2025Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfacescited by 0
  11. 2025Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversationscited by 0
  12. 2025From keywords to semantics: Perceptions of large language models in data discoverycited by 0
  13. 2025Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Developmentcited by 0
  14. 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussionscited by 0
  15. 2025The Personalization Paradox: Semantic Loss vs. Reasoning Gains in Agentic AI Q&Acited by 0
  16. 2026When 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 Adoptioncited by 0
  17. 2026Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparencycited by 0
  18. 2026From Performance to Purpose: A Sociotechnical Taxonomy for Evaluating Large Language Model Utilitycited by 0
  19. 2026Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Makingcited by 0
  20. 2026Can Virtual Agents Care? Designing an Empathetic and Personalized LLM-Driven Conversational Agentcited by 0
  21. 2026Position: We Need Practical AI Alignment Methods to Mirror Human Reasoningcited by 0