Scope and obligations

Procurement-focused operational guide accompanying OMB M-24-10 and the broader EO 14110 / EO 14179 federal-AI policy stack. Provides agencies with: (1) market intelligence on the GSA Multiple Award Schedule special item numbers covering AI services (54151S IT Professional Services + the newer AI / Generative AI SINs); (2) sample acquisition language for responsible-AI requirements (bias-testing, transparency, evaluation, security); (3) supply-chain risk-management considerations including model-provenance and dependency disclosure; (4) requirements derivation guidance for safety- and rights-impacting AI per OMB M-24-10 Attachment 1. The guide is non-binding on its own but agencies typically incorporate its language into solicitation packages.

GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide addresses 3 contested AI-governance topics explicitly, 3 via general principles,.

Topics governed

• governs
  Foundation Models / GPAI— Sections covering generative AI vendor evaluation + model-provenance disclosure requirements in solicitation language

  §Generative AI acquisition guidanceparaphrase

  Faithful summary: the guide treats generative-AI and foundation-model acquisition as a discrete category, with sample solicitation language for evaluating model provenance, capabilities, and vendor documentation.

  source ↗
• governs
  Compute-Threshold Reporting— Guide enumerates GSA Multiple Award Schedule SINs covering AI / Generative AI services so agencies can report on AI acquisitions through the SIN structure

  §GSA MAS AI SINsparaphrase

  Faithful summary: the guide enumerates GSA Multiple Award Schedule Special Item Numbers covering AI and generative-AI services so agencies can route and report AI acquisitions through the SIN structure.

  source ↗
• governs
  Transparency Obligations— Sample solicitation language requires vendor disclosure of training-data provenance, evaluation results, and model documentation

  §Vendor disclosure / evaluation criteriaparaphrase

  Faithful summary: sample solicitation language directs agencies to require vendor disclosure of training-data provenance, evaluation and benchmarking results, and model documentation as part of AI acquisition.

  source ↗
• implicit
  Individual Redress— Guide references OMB M-24-10 Attachment 1 minimum practices including human-consideration + remedy for rights-impacting AI
• implicit
  Training-Data Rights— Supply-chain risk-management considerations include training-data provenance + dependency disclosure
• implicit
  National Security Carveouts in AI Regulation— Guide references existing federal supply-chain risk-management framework (FAR Part 4 Subpart 4.21) which carries national-security overlays

Cross-jurisdiction comparison

How peer instruments treat the topics GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide governs.

°= industry self-imposed voluntary framework. Comparing a voluntary code's "governs" tint with a binding regulation's "governs" tint flattens the legal-force distinction; use the instrument-page banner for the operative status of each.

How to cite this article

Policy Window. (2024). GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide [Wiki article — Instrument]. https://policywindow.org/wiki/gsa-ai-acquisition-guide

Policy Window. 2024. "GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide." Wiki article (Instrument). https://policywindow.org/wiki/gsa-ai-acquisition-guide.

@misc{policywindow-gsa-ai-acquisition-guide,
  title  = {GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide},
  author = {Policy Window},
  year   = {2024},
  howpublished = {GSA, Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide (Sept. 24, 2024)},
  url    = {https://policywindow.org/wiki/gsa-ai-acquisition-guide},
  note   = {Primary source: https://www.gsa.gov/technology/government-it-initiatives/artificial-intelligence/ai-acquisition-resources}
}

Related instruments

• EU AI Act · EU
• Executive Order 14110 on Safe, Secure, Trustworthy AI · US
• G7 Hiroshima AI Process Code of Conduct · G7
• NIST AI Risk Management Framework · US
• Seoul Declaration on Safe, Innovative and Inclusive AI · global
• NIST AI RMF Generative AI Profile · US
• California SB-1047: Safe and Secure Innovation for Frontier AI Models Act · US
• Brazil AI Bill (PL 2338/2023) · BR
• Anthropic Responsible Scaling Policy (RSP) v2 · US
• Meta Frontier AI Framework · US
• White House Voluntary AI Commitments · US
• Singapore Model AI Governance Framework for Generative AI · SG
• Japan METI AI Guidelines for Business · JP
• EU General-Purpose AI Code of Practice · EU
• OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI) · US

Evidence base

Academic & grey-literature sources on the topics this instrument addresses (not commentary on the instrument itself) — catalogued metadata with a primary link, no LLM summaries (charter §7). Browse the full literature index.

• Model Card ↗PreprintMitchell et al. (2019), 'Model Cards for Model Reporting,' FAccT '19
• Deceptive Alignment ↗PreprintHubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
• Mesa-Optimization ↗PreprintHubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
• Scalable Oversight ↗PreprintChristiano, P., Shlegeris, B., Amodei, D. (2018), 'Supervising Strong Learners by Amplifying Weak Experts.'
• Capability Elicitation ↗PreprintQi, 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!'
• Dual-Use Research Norms (DURC for AI) ↗PreprintSolaiman, I., et al. (2019), 'Release Strategies and the Social Impacts of Language Models' — the canonical articulation of structured-access norms for foundation models.
• Policy Instrument ↗Peer-reviewedLascoumes, 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.
• Training-Data Attribution ↗PreprintGrosse, 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.
• Prompt Injection ↗PreprintGreshake, 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.'
• Agentic AI System ↗PreprintYao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y. (2022), 'ReAct: Synergizing Reasoning and Acting in Language Models.'
• Tool-Use Safety ↗PreprintWallace, 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.
• Multi-Turn Evaluation ↗PreprintZheng, L., et al. (2023), 'Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena' — operationalises the multi-turn evaluation protocol for foundation models.
• Data Poisoning ↗PreprintCarlini, N., et al. (2024), 'Poisoning Web-Scale Training Datasets is Practical' — establishes practical feasibility of poisoning frontier-model training corpora.
• Model Distillation Risk ↗PreprintHinton, 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).
• Jailbreak Resistance ↗PreprintZou, 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.
• Model-Merging Risk ↗PreprintBhardwaj, 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.
• Inference-Time Compute ↗PreprintSnell, 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.
• Sandbagging ↗Preprintvan der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., Ward, F. (2024), 'AI Sandbagging: Language Models can Strategically Underperform on Evaluations.'
• Hallucination ↗PreprintJi, Z., et al. (2023), 'Survey of Hallucination in Natural Language Generation,' ACM Computing Surveys 55(12): 1-38.
• In-Context Learning ↗PreprintBrown, T., et al. (2020), 'Language Models are Few-Shot Learners' (GPT-3 paper) — the canonical articulation of in-context learning as an emergent capability.
• Retrieval-Augmented Generation (RAG) ↗PreprintLewis, P., et al. (2020), 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,' NeurIPS — the canonical articulation of RAG.
• AI Risk Management Framework | NIST ↗Standards body✦ AIUS voluntary AI risk-management framework (Govern/Map/Measure/Manage).
• ISO/IEC JTC 1/SC 42 - Artificial intelligence ↗Standards body✦ AIInternational committee developing AI standards.
• ISO - Security, safety and risk ↗Standards body✦ AIISO security, safety & risk standards portal.
• OECD AI Incidents Monitor, an evidence base for trustworthy AI - OECD.AI ↗Incident database✦ AIOECD tracker of real-world AI incidents and hazards.
• AI Index | Stanford HAI ↗Research institute✦ AIStanford HAI's annual data report on the state of AI.
• Regulation, Policy, Governance | Stanford HAI ↗Research institute✦ AIStanford HAI's regulation & governance research hub.
• Papers & Reports | Epoch AI ↗Research institute✦ AIEpoch AI research on compute, scaling trends & frontier models.
• Artificial Intelligence ↗Research institute✦ AIUS National Academies' AI consensus-study hub.
• Capturing the Potential of Generative AI’s Use in Health and Medicine Requires Collaboration and Oversight, Consideration of Risks, Says NAM Special Publication ↗Research institute✦ AINAM special publication on generative AI in health & medicine.
• One Hundred Year Study on Artificial Intelligence (AI100) ↗Research institute✦ AIStanford's standing century-long study of AI's societal impact.
• National Bureau of Economic Research | NBER ↗Working paper✦ AIUS National Bureau of Economic Research.
• Featured Working Papers Archive | NBER ↗Working paper✦ AINBER featured economics working papers (incl. AI & labor).
• Measuring up | Ada Lovelace Institute ↗Civil society✦ AIAda Lovelace Institute policy briefing.
• Publications - AlgorithmWatch ↗Civil society✦ AIReports on automated decision-making and its societal impact.
• Anthropomorphic AI terms create gaps in accountability | Brookings ↗Think tank✦ AICommentary on how anthropomorphic AI language obscures accountability.

References

1. GSA, Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide (Sept. 24, 2024)
2. Sections covering generative AI vendor evaluation + model-provenance disclosure requirements in solicitation language
3. Guide enumerates GSA Multiple Award Schedule SINs covering AI / Generative AI services so agencies can report on AI acquisitions through the SIN structure
4. Sample solicitation language requires vendor disclosure of training-data provenance, evaluation results, and model documentation
5. Guide references OMB M-24-10 Attachment 1 minimum practices including human-consideration + remedy for rights-impacting AI
6. Supply-chain risk-management considerations include training-data provenance + dependency disclosure
7. Guide references existing federal supply-chain risk-management framework (FAR Part 4 Subpart 4.21) which carries national-security overlays