References
Status: Non-normative Last Updated: 2026-02-07
This document provides a consolidated reference list for the AIPolicy research materials. References are grouped by category and formatted in a standard academic citation style. URLs are provided where available and were last verified as of the date above.
AI Alignment and Training Methods
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., Kerr, J., Mueller, J., Ladish, J., Landau, J., Ndousse, K., Lukosuite, K., Lovitt, L., Sellitto, M., Elhage, N., Schiefer, N., Mercado, N., DasSarma, N., Lasenby, R., Larson, R., Ringer, S., Johnston, S., Kravec, S., El Showk, S., Fort, S., Lanham, T., Telleen-Lawton, T., Conerly, T., Henighan, T., Hume, T., Bowman, S., Hatfield-Dodds, Z., Mann, B., Amodei, D., Joseph, N., McCandlish, S., Brown, T., & Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. arXiv preprint, arXiv:2212.08073. https://arxiv.org/abs/2212.08073
Christiano, P. F., Leike, J., Brown, T., Marber, M., Legg, S., & Amodei, D. (2017). Deep Reinforcement Learning from Human Preferences. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03741
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35. https://arxiv.org/abs/2203.02155
AI Documentation Standards
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT)*, 220--228. https://arxiv.org/abs/1810.03993
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daume III, H., & Crawford, K. (2021). Datasheets for Datasets. Communications of the ACM, 64(12), 86--92. https://arxiv.org/abs/1803.09010
International Governance Frameworks
European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L Series. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381137
OECD. (2019). Recommendation of the Council on Artificial Intelligence. OECD/LEGAL/0449. Organisation for Economic Co-operation and Development. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
Council of Europe. (2024). Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law. Council of Europe Treaty Series No. 225. https://www.coe.int/en/web/artificial-intelligence/the-framework-convention-on-artificial-intelligence
National Institute of Standards and Technology. (2023). AI Risk Management Framework (AI RMF 1.0). NIST AI 100-1. U.S. Department of Commerce. https://www.nist.gov/artificial-intelligence/executive-order-safe-secure-and-trustworthy-artificial-intelligence
Web Standards and Specifications
Krawczyk, J. & Novak, J. (2024). llms.txt: A Proposal for Standardizing LLM Access to Websites. https://llmstxt.org/
Spawning AI. (2023). ai.txt Specification. https://site.spawning.ai/spawning-ai-txt
Nottingham, M. (2019). Well-Known Uniform Resource Identifiers (URIs). RFC 8615. Internet Engineering Task Force. https://www.rfc-editor.org/rfc/rfc8615
Foudil, E. & Shafranovich, Y. (2022). A File Format to Aid in Security Vulnerability Disclosure. RFC 9116. Internet Engineering Task Force. https://www.rfc-editor.org/rfc/rfc9116
Schema.org Community Group. (n.d.). Schema.org. https://schema.org/
Koster, M. & Illyes, G. (2022). Robots Exclusion Protocol. RFC 9309. Internet Engineering Task Force. https://www.rfc-editor.org/rfc/rfc9309
Training Data and Corpus Research
Dodge, J., Sap, M., Marasovic, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021). Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1286--1305. https://arxiv.org/abs/2104.08758
Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2022). Deduplicating Training Data Makes Language Models Better. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 8424--8445. https://arxiv.org/abs/2107.06499