Benchmarking Trust: A Metric for Trustworthy Machine Learning
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The FRIES framework's methodology, inspired by FMEA, relies on averaging individual scores, which fails to capture nuanced expert perspectives or account for the relative importance of different criteria.
“However, while the FRIES framework provides a solid foundation, its methodology, inspired by failure mode and effects analysis (FMEA), has notable limitations. It relies on averaging individual scores, which fails to capture the nuanced perspectives of experts or account for the relative importance of different criteria.”
From Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making (2025) · cites this paper as [15]
The FRIES trust score framework defines five trust dimensions (fairness, robustness, integrity, explainability, and safety) based on an extensive literature review.
“This study is inspired by the FRIES trust score framework proposed by [15], which evaluates the trustworthiness of machine learning models and datasets. The framework defines five trust dimensions (fairness, robustness, integrity, explainability, and safety) based on an extensive literature review.”
From Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making (2025) · cites this paper as [15]
The FRIES framework's evaluation methodology based on simple averaging does not capture expert hesitation or the relative importance of different criteria.
“However, its evaluation methodology-based on simple averaging-does not capture expert hesitation or the relative importance of different criteria.”
From Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making (2025) · cites this paper as [15]
Focusing on the established five-criteria set follows prior work and ensures that the evaluation framework remains tractable for expert judgment while still covering essential aspects of trustworthiness.
“Focusing on this established five-criteria set not only follows prior work [15] but also ensures that the evaluation framework remains tractable for expert judgment while still covering the essential aspects of trustworthiness.”
From Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making (2025) · cites this paper as [15]
Cites
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · cited 4× · see citing works
“In contrast, [6, 25, 48, 50] understand the concept as the provision of reasons for the decisions (i.e., predictions) that have been made by a given model.”
InfluentialExplaining models: an empirical study of how explanations impact fairness judgment
Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302310 · Cited by 286 · see citing works
Wischnewski, M., Krämer, N., & Müller, E. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581197 · Cited by 146 · cited 1× · see citing works
“This section thus provides an overview of the key aspects of trustworthy machine learning encountered in literature, namely fairness, robustness, integrity, explainability, and safety, that are most prominent in the relevant literature [25,42,50,51].”
Self-citationThe Self-Perception and Political Biases of ChatGPT
Rutinowski, J., Franke, S., Endendyk, J., Dormuth, I., Roidl, M., & Pauly, M. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/7115633 · Cited by 98 · see citing works
Self-citation
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Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making
Aksoy, M., Adem, A., & Dağdeviren, M. (2025) · IEEE Access · doi:10.1109/access.2025.3612568 · Cited by 10 · see citing works
“Unlike FRIES [15], which relies on averaging individual scores, our hesitant fuzzy AHP approach systematically incorporates expert hesitation through linguistic term sets and provides consistency checks in pairwise comparisons.”
Influential
- Authors
- Rutinowski, J., Klüttermann, S., Endendyk, J., Reining, C., & Müller, E.
- Affiliations
- TU Dortmund University (DE)
- Corresponding author
- Rutinowski, Jérôme
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 287-307
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W4400439290
- Cited by
- 4
- References
- 7
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Communications in computer and information science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- trustworthy machine learning, fairness, robustness, data integrity, explainability, safety, expert evaluation, benchmarking
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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.
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