Can ex ante conformity assessment regulations contribute to trustworthy foundation models? An evolutionary game analysis from an innovation ecosystem perspective
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Continuous supervision and real-time evaluation in sandboxes generate empirical insights that help regulators detect latent risks, emergent behaviors, systemic biases, and complex socio-technical dynamics that often elude theoretical models and ex-ante assessments.
“Continuous supervision and real-time evaluation generate empirical insights that help regulators detect latent risks, emergent behaviors, systemic biases, and complex socio-technical dynamics that often elude theoretical models and ex-ante assessments (Zhang et al. 2025; Scherer 2016).”
From Bridging Liability Gaps in the Age of AI: The Case for No-Fault Compensation Schemes (2025) · cites this paper as Zhang et al. 2025
Cites
Unpacking the innovation ecosystem construct: Evolution, gaps and trends
Gomes, L. A. D. V., Facin, A. L. F., Salerno, M. S., & Ikenami, R. K. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.11.009 · Cited by 836 · see citing works
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800 · 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 · see citing works
Regulating ChatGPT and other Large Generative AI Models
Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466 · see citing works
Establishing the rules for building trustworthy AI
Floridi, L. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0055-y · Cited by 394 · see citing works
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.07213 · Cited by 306 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bridging Liability Gaps in the Age of AI: The Case for No-Fault Compensation Schemes
Tran, H.-C. (2025) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v8i3.36797 · Cited by 1 · see citing works
- Authors
- Zhang, X., Zhou, W., Hu, W., Zhou, S., Hu, X., & Yang, L.
- Affiliations
- Tongji University (CN)
- Beihang University (CN)
- Capital University of Economics and Business (CN)
- North China Electric Power University (CN)
- Corresponding author
- Yang, Linchao
- Venue
- Technology in Society
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 82
- Pages
- 102900
- ISSN
- 0160-791X (ISSN-L)
- OpenAlex
- W4409301307
- Cited by
- 1
- References
- 6
- Citations by year
- 2026: 1
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Technology in Society (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (YWF-23-L-933)
- National Natural Science Foundation of China (72371013, 72101164, 72401097)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Strategy and Management › Regulation and Compliance Studies
- Keywords
- foundation models, trustworthy AI, EU AI Act, AI regulation, innovation ecosystems, evolutionary game theory, self-assessment, independent evaluation, market incentives, high-risk AI systems
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
