Referenzen

Status: Nicht-normativ Letzte Aktualisierung: 2026-02-07

Dieses Dokument stellt eine konsolidierte Referenzliste für die AIPolicy-Forschungsmaterialien bereit. Referenzen sind nach Kategorie gruppiert und in einem standardmäßigen akademischen Zitierstil formatiert. URLs sind, sofern verfügbar, angegeben und wurden zuletzt zum oben genannten Datum überprüft.

KI-Alignment und Trainingsmethoden#

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

KI-Dokumentationsstandards#

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

Internationale Governance-Rahmenwerke#

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

Webstandards und Spezifikationen#

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

Trainingsdaten- und Korpusforschung#

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