The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Trust in AI: progress, challenges, and future directions
Open accessAbstract
The increasing use of artificial intelligence (AI) systems in our daily lives through various applications, services, and products highlights the significance of trust and distrust in AI from a user perspective. AI-driven systems have significantly diffused into various aspects of our lives, serving as beneficial “tools” used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust and distrust in AI serve as regulators and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, a variety of studies focused on the different dimensions of trust and distrust in AI and its relevant considerations. In this systematic literature review, after conceptualizing trust in the current AI literature, we will investigate trust in different types of human–machine interaction and its impact on technology acceptance in different domains. Additionally, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, along with some trustworthy measurements. Moreover, we examine major trust-breakers in AI (e.g., autonomy and dignity threats) and trustmakers; and propose some future directions and probable solutions for the transition to a trustworthy AI.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 2×
“Although transparency and explainability have been usually categorized under the same ethical principle (Jobin et al., 2019a), it is essential to distinguish between these two different topics before extensive interchangeable misuse of them.”
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Tabassi, E. (2023) · doi:10.6028/nist.ai.100-1 · Cited by 2,951
Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · cited 1×
“For a comprehensive review of AI ethics guidelines, the practical implementation of AI and ethics, and advancements in AI ethics, interested readers are referred to (Hagendorff, 2020).”
The Ethics of AI Ethics -- An Evaluation of Guidelines
Hagendorff, T. (2019) · arXiv · doi:10.48550/arxiv.1903.03425 · Cited by 1,907 · cited 1×
“For a comprehensive review of AI ethics guidelines, the practical implementation of AI and ethics, and advancements in AI ethics, interested readers are referred to (Hagendorff, 2020).”
Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation
Culnan, M. J., & Armstrong, P. K. (1999) · Organization Science · doi:10.1287/orsc.10.1.104 · Cited by 1,870
Cited by
AI agents and agentic systems: a multi-expert analysis
Hughes, L., Dwivedi, Y. K., Malik, T., Shawosh, M., Albashrawi, M. A., Jeon, I., Dutot, V., Appanderanda, M., Crick, T., De’, R., et al. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2483832 · Cited by 168
Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models
Ferdaus, M. M., Abdelguerfi, M., Ioup, E., Niles, K. N., Pathak, K., & Sloan, S. (2024) · arXiv · doi:10.48550/arxiv.2407.13934 · Cited by 80
ADABARA, I., Olaniyi Sadiq, B., Nuhu Shuaibu, A., Ibrahim Danjuma, Y., & Maninti, V. (2025) · F1000Research · doi:10.12688/f1000research.169927.1 · Cited by 38
Trusting AI: does uncertainty visualization affect decision-making?
Reyes, J., Batmaz, A. U., & Kersten-Oertel, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1464348 · Cited by 27
Editorial: Framing the Future with a Research Agenda for Artificial Intelligence in Higher Education
Fitzgerald, R., Amantha Kumar, J., Roe, J., Roehrer, E., & Yang, J. (2025) · Journal of University Teaching and Learning Practice · doi:10.53761/jwt7ra63 · Cited by 8
Metadata
- Authors
- Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H.
- Venue
- Humanities and Social Sciences Communications
- Published
- 2024-11-18
- Volume
- 11
- Issue
- 1
- ISSN
- 2055-1045 (ISSN-L)
- 2662-9992
- OpenAlex
- W4404470920
- Cited by
- 507
- References
- 43
- FWCI
- 209.28
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Trust in AI: progress, challenges, and future directions. https://app.cochat.ai/papers/10.1057/s41599-024-04044-8
- The paper
- Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: progress, challenges, and future directions. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-024-04044-8
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
