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FactSheets: Increasing trust in AI services through supplier's declarations of conformity
Open accessAbstract
Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers’ trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multidimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers’ trust. In this article, inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI in the Appendix of this article.
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“AI documentation is advocated to increase the transparency and facilitate understanding of AI [35,36,40,75,76,82], and is embodied in recently proposed features of AI Factsheets [8,81] and Model Cards [64].”
From Investigating Explainability of Generative AI for Code through Scenario-based Design (2022) · cites this paper as [8]
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“There is some work on fairness relating to AI services, though this tends to focus on either exposing or 'auditing' certain AI services [22,38,126,133], or devising tools, methods and interventions relevant for those building (rather than using) such services [12,96,106,128].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [12]
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“There appears a clear role for adopting and extending various documentation approaches, be they relating to data and models (such as datasheets [53], model cards [106] and factsheets [12]), their interactions and interconnections (e.g.decision provenance [139]), and broader, holistic socio-technical system reviews (e.g.reviewability [27] and traceability [86]).”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [12]
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Barclay, I., Preece, A. D., Taylor, I., & Verma, D. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04253 · Cited by 13
Burr, C., & Leslie, D. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3937983 · Cited by 13
Li, Y., & Goel, S. (2025) · Advances in Accounting · doi:10.1016/j.adiac.2025.100842 · Cited by 21
Ethics of Trust/worthiness in Autonomous Systems: a scoping review.
Smith, H., Manzini, A., Kennedy, M., & Ives, J. (2023) · International Symposium on Trustworthy Autonomous Systems (TAS) · doi:10.1145/3597512.3600207 · Cited by 14
Towards more sustainable and trustworthy reporting in machine learning
Fischer, R., Liebig, T., & Morik, K. J. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01020-3 · Cited by 12
Design, Development, and Deployment of Context-Adaptive AI Systems for Enhanced User Adoption
Lee, C. P. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3638195 · Cited by 15
A comprehensive review of techniques for documenting artificial intelligence
Königstorfer, F. (2024) · Digital Policy Regulation and Governance · doi:10.1108/dprg-01-2024-0008 · Cited by 15
An Ontology for Standardising Trustworthy AI
Lewis, D., Filip, D., & J. Pandit, H. (2021) · Factoring Ethics in Technology, Policy Making, Regulation and AI · doi:10.5772/intechopen.97478 · Cited by 13
Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector
Lewis, G. A., Bellomo, S., & Galyardt, A. (2019) · arXiv · doi:10.48550/arxiv.1910.06136 · Cited by 8
On the State of Reporting in Crowdsourcing Experiments and a Checklist to Aid Current Practices
Ramírez, J., Sayin, B., Baez, M., Casati, F., Cernuzzi, L., Benatallah, B., & Demartini, G. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479531 · Cited by 4
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Kroll, J. A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445937 · Cited by 2
Can AI provenance inform archival provenance?
Corry, F. (2026) · Cambridge Forum on AI: Culture and Society · doi:10.1017/cfc.2026.10025
“Related frameworks, like FactSheets (Arnold et al. 2019), Data Cards (Pushkarna et al. 2022) and Dataset Nutrition Labels (Holland et al. 2020), have encouraged provenance documentation similar to Datasheets while emphasizing legibility, conciseness and “user friendliness” to the dataset user.”
Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI
Kuehnert, B., Johnson, N., Dotan, R., & Heidari, H. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3812220
“The GovAI Factsheet builds on earlier documentation initiatives in responsible AI, such as model cards [51] or IBM’s original AI factsheet for AI developers [6], while adapting the concept to the unique context of public-sector procurement.”
A federated architecture for sector-led AI governance: lessons from India
Agarwal, A., & Nene, M. J. (2026) · Transforming Government: People, Process and Policy · doi:10.1108/tg-09-2025-0310
Metadata
- Authors
- Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., Reimer, D., Richards, J., Tsay, J., & Varshney, K. R.
- Venue
- IBM Journal of Research and Development
- Publisher
- IBM
- Published
- 2019-07-01
- Volume
- 63
- Issue
- 4/5
- Pages
- 6:1-6:13
- ISSN
- 0018-8646 (ISSN-L)
- OpenAlex
- W2911599914
- Cited by
- 434
- References
- 44
- Citations by year
- 2026: 47 · 2025: 44 · 2024: 62 · 2023: 93 · 2022: 58 · 2021: 78 · 2020: 32 · 2019: 18
- FWCI
- 81.25
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IBM Journal of Research and Development (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- trust in AI, AI safety, AI security, AI provenance, algorithmic fairness, explainable AI, consumer trust in AI
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 ›
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