mlsanityData to 13 Oct 2026

The relationship between trust in AI and trustworthy machine learning technologies

arXiv 1912.00782 ↗First posted 27 Nov 2019cs.CY cs.AI cs.LGcited by 78

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Abstract (arXiv)

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.

Key facts. Cited by 78 papers in the map (data through 13 Oct 2026). First posted on arXiv 27 Nov 2019.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 12; 2022: 16; 2023: 20; 2024: 15; 2025: 10; 2026: 5.

78 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2024Instruction Backdoor Attacks Against Customized LLMscited by 61
  3. 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
  4. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  5. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  6. 2024A Review on Machine Unlearningcited by 32
  7. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  8. 2022AI-Augmented Business Process Management Systems: A Research Manifestocited by 28
  9. 2021The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Resultscited by 27
  10. 2023A Systematic Review on Fostering Appropriate Trust in Human-AI Interactioncited by 21
  11. 2021Learning to run a power network with trustcited by 17
  12. 2021Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AIcited by 16
38 more
  1. 2021A Survey on AI Assurancecited by 16
  2. 2022Towards Explainable Evaluation Metrics for Natural Language Generationcited by 15
  3. 2023Towards Explainable Evaluation Metrics for Machine Translationcited by 15
  4. 2023Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Applicationcited by 14
  5. 2022Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficientcited by 13
  6. 2024A Survey of Controllable Learning: Methods and Applications in Information Retrievalcited by 12
  7. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9
  8. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  9. 2021Toward a Taxonomy of Trust for Probabilistic Machine Learningcited by 8
  10. 2024Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and Englishcited by 8
  11. 2023Out-of-distribution detection algorithms for robust insect classificationcited by 7
  12. 2023Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunitiescited by 7
  13. 2023Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systemscited by 7
  14. 2024Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attributioncited by 7
  15. 2024To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systemscited by 7
  16. 2022Interactive Machine Learning: A State of the Art Reviewcited by 6
  17. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 6
  18. 2023Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applicationscited by 6
  19. 2025Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articlescited by 6
  20. 2021To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictionscited by 5
  21. 2023Users are the North Star for AI Transparencycited by 5
  22. 2023Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Studycited by 5
  23. 2024Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPTcited by 5
  24. 2022Trust in AI and Implications for the AEC Research: A Literature Analysiscited by 4
  25. 2022Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variablescited by 4
  26. 2023Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthinesscited by 4
  27. 2023Trustworthy Federated Learning: A Surveycited by 4
  28. 2025Verification and Validation for Trustworthy Scientific Machine Learningcited by 4
  29. 2025Human-AI Governance (HAIG): A Trust-Utility Approachcited by 4
  30. 2025Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Makingcited by 4
  31. 2025Understanding Human-AI Trust in Educationcited by 4
  32. 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approachcited by 3
  33. 2022Design Guidelines for Inclusive Speaker Verification Evaluation Datasetscited by 3
  34. 2023VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Realitycited by 3
  35. 2024Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligencecited by 3
  36. 2021Building Legal Datasetscited by 2
  37. 2022TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learningcited by 2
  38. 2023ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescriptioncited by 2

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