mlsanityData to 7 Oct 2026

Trust in AI: Progress, Challenges, and Future Directions

arXiv 2403.14680 ↗First posted 12 Mar 2024cs.CY cs.AIcited by 35

Not on the map. Why?

Abstract (arXiv)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 47 of its 288 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

47 of 288 references.

References · 47

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  4. 2016The Mythos of Model Interpretabilitycited by 716
  5. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  6. 2020Artificial Intelligence, Values and Alignmentcited by 443
  7. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 318
  8. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  9. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  10. 2018Techniques for Interpretable Machine Learningcited by 237
  11. 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 228
  12. 2015Visualizing and Understanding Neural Models in NLPcited by 199
35 more
  1. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  2. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
  3. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  4. 2017What do we need to build explainable AI systems for the medical domain?cited by 127
  5. 2020Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challengescited by 120
  6. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
  7. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  8. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  9. 2020Interpretability of machine learning based prediction models in healthcarecited by 75
  10. 2019Quantifying Interpretability and Trust in Machine Learning Systemscited by 49
  11. 2018Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systemscited by 47
  12. 2019How model accuracy and explanation fidelity influence user trustcited by 44
  13. 2021Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lenscited by 35
  14. 2021Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactionscited by 35
  15. 2019Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forwardcited by 33
  16. 2018FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformitycited by 31
  17. 2019Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretabilitycited by 22
  18. 2021The Sanction of Authority: Promoting Public Trust in AIcited by 19
  19. 2021Explainable Autonomous Robots: A Survey and Perspectivecited by 15
  20. 2019Explainable Text-Driven Neural Network for Stock Predictioncited by 13
  21. 2019In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processescited by 13
  22. 2020Modeling Trust in Human-Robot Interaction: A Surveycited by 12
  23. 2020Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluationscited by 8
  24. 2021More Similar Values, More Trust? -- the Effect of Value Similarity on Trust in Human-Agent Interactioncited by 7
  25. 2021Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Searchcited by 7
  26. 2020Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategiescited by 5
  27. 2021Will We Trust What We Don't Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AIcited by 5
  28. 2018ClinicalVis: Supporting Clinical Task-Focused Design Evaluationcited by 4
  29. 2018TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environmentscited by 4
  30. 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learningcited by 4
  31. 2018Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectivenesscited by 2
  32. 2021A Conceptual Framework for Establishing Trust in Real World Intelligent Systemscited by 2
  33. 2021Understanding Relations Between Perception of Fairness and Trust in Algorithmic Decision Makingcited by 2
  34. 2021A Survey on Trust Metrics for Autonomous Robotic Systemscited by 1
  35. 2024Evaluating if trust and personal information privacy concerns are barriers to using health insurance that explicitly utilizes AIcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 17; 2026: 17.

35 papers.

Sort:

By area:

  1. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  2. 2026Intelligent AI Delegationcited by 16
  3. 2025From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphorscited by 9
  4. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  5. 2026Position: Human-Centric AI Requires a Minimum Viable Level of Human Understandingcited by 6
  6. 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governancecited by 5
  7. 2025Understanding Human-AI Trust in Educationcited by 4
  8. 2025Selecting the Right LLM for eGov Explanationscited by 3
  9. 2026A Framework for Longitudinal Health AI Agentscited by 3
  10. 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanationscited by 2
  11. 2025Multi-Stakeholder Alignment in LLM-Powered Collaborative AI Systems: A Multi-Agent Framework for Intelligent Tutoringcited by 2
  12. 2025Beyond Chat: a Framework for LLMs as Human-Centered Support Systemscited by 2
23 more
  1. 2025Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Studycited by 2
  2. 2025Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directionscited by 1
  3. 2025Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)cited by 1
  4. 2025Agentic AI with Orchestrator-Agent Trust: A Modular Visual Classification Framework with Trust-Aware Orchestration and RAG-Based Reasoningcited by 1
  5. 2025DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflowcited by 1
  6. 2026Beyond Abstract Compliance: Operationalising trust in AI as a moral relationshipcited by 1
  7. 2026An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systemscited by 1
  8. 2026Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Frameworkcited by 1
  9. 2025Towards Computer-Using Personal Agentscited by 0
  10. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  11. 2025Preliminary Quantitative Study on Explainability and Trust in AI Systemscited by 0
  12. 2025Plausibility as Failure: How LLMs and Humans Co-Construct Epistemic Errorcited by 0
  13. 2026Adaptive Trust Metrics for Multi-LLM Systems: Enhancing Reliability in Regulated Industriescited by 0
  14. 2026Eliciting Trustworthiness Priors of Large Language Models via Economic Gamescited by 0
  15. 2026Botson: An Accessible and Low-Cost Platform for Social Robotics Researchcited by 0
  16. 2026Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulationcited by 0
  17. 2026Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interactioncited by 0
  18. 2026Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?cited by 0
  19. 2026Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerabilitycited by 0
  20. 2026AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systemscited by 0
  21. 2026Psychological Competence as a Missing Dimension in AI Evaluationcited by 0
  22. 2026Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AIcited by 0
  23. 2026Addressing Trust in AI Systems through Education: A Didactic Perspectivecited by 0