Literature & evidence base

Lascoumes, P. & Le Galès, P. (2007). Introduction: Understanding Public Policy through Its Instruments — From the Nature of Instruments to the Sociology of Public Policy Instrumentation. Governance 20(1): 1-21. See also Hood (1983) The Tools of Government, ch. 1-2; Salamon (2002) The Tools of Government: A Guide to the New Governance, pp. 1-47; Howlett (2011) Designing Public Policies, ch. 3-5.

Mitchell et al. (2019), 'Model Cards for Model Reporting,' FAccT '19

Hubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'

Hubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'

Christiano, P., Shlegeris, B., Amodei, D. (2018), 'Supervising Strong Learners by Amplifying Weak Experts.'

Qi, X., Zeng, Y., Xie, T., Chen, P.-Y., Jia, R., Mittal, P., Henderson, P. (2023), 'Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!'

Solaiman, I., et al. (2019), 'Release Strategies and the Social Impacts of Language Models' — the canonical articulation of structured-access norms for foundation models.

Grosse, R., et al. (2023), 'Studying Large Language Model Generalization with Influence Functions' (Anthropic) — the canonical articulation of scalable influence-function-based attribution for foundation models.

Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., Fritz, M. (2023), 'Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.'

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y. (2022), 'ReAct: Synergizing Reasoning and Acting in Language Models.'

Wallace, E., et al. (2024), 'The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions' (OpenAI) — the canonical industry articulation of instruction-channel hierarchy as a tool-use-safety defence.

Zheng, L., et al. (2023), 'Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena' — operationalises the multi-turn evaluation protocol for foundation models.

Carlini, N., et al. (2024), 'Poisoning Web-Scale Training Datasets is Practical' — establishes practical feasibility of poisoning frontier-model training corpora.

Hinton, G., Vinyals, O., Dean, J. (2015), 'Distilling the Knowledge in a Neural Network' — the foundational distillation paper; the governance-relevant adaptation runs through Alpaca/Vicuna (2023) and DeepSeek-R1 (2025).

Zou, A., Wang, Z., Kolter, J. Z., Fredrikson, M. (2023), 'Universal and Transferable Adversarial Attacks on Aligned Language Models' — the canonical demonstration that gradient-based suffix attacks transfer across aligned LLMs.

Bhardwaj, R., et al. (2024), 'Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic' — canonical demonstration that safety training is not preserved under task arithmetic / merging.

Snell, C., Lee, J., Xu, K., Kumar, A. (2024), 'Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters' — establishes inference-time-compute scaling as a first-class capability lever.

van der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., Ward, F. (2024), 'AI Sandbagging: Language Models can Strategically Underperform on Evaluations.'

Ji, Z., et al. (2023), 'Survey of Hallucination in Natural Language Generation,' ACM Computing Surveys 55(12): 1-38.

Brown, T., et al. (2020), 'Language Models are Few-Shot Learners' (GPT-3 paper) — the canonical articulation of in-context learning as an emergent capability.

Lewis, P., et al. (2020), 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,' NeurIPS — the canonical articulation of RAG.

✦ AIUS National Bureau of Economic Research.

✦ AINBER featured economics working papers (incl. AI & labor).

✦ AIStanford HAI's annual data report on the state of AI.

✦ AIStanford HAI's regulation & governance research hub.

✦ AIEpoch AI research on compute, scaling trends & frontier models.

✦ AIUS National Academies' AI consensus-study hub.

✦ AINAM special publication on generative AI in health & medicine.

✦ AIStanford's standing century-long study of AI's societal impact.

✦ AICommentary on how anthropomorphic AI language obscures accountability.

✦ AIAda Lovelace Institute policy briefing.

✦ AIReports on automated decision-making and its societal impact.

✦ AIUS voluntary AI risk-management framework (Govern/Map/Measure/Manage).

✦ AIInternational committee developing AI standards.

✦ AIISO security, safety & risk standards portal.

✦ AIOECD tracker of real-world AI incidents and hazards.