mlsanityData to 7 Oct 2026

AI Governance through Markets

arXiv 2501.17755 ↗First posted 29 Jan 2025econ.GN cs.AI q-fin.ECcited by 5

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Abstract (arXiv)

This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 27 of its 155 references that are in the map. First posted on arXiv 29 Jan 2025.

Builds on

27 of 155 references.

References · 27

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
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  2. 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
  3. 2024LLM Pruning and Distillation in Practice: The Minitron Approachcited by 82
  4. 2023The Foundation Model Transparency Indexcited by 63
  5. 2023Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filteringcited by 62
  6. 2024Understanding LLMs: A Comprehensive Overview from Training to Inferencecited by 58
  7. 2024Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Trainingcited by 42
  8. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  9. 2022On the Impossible Safety of Large AI Modelscited by 27
  10. 2023Large Language Model (LLM) Bias Index -- LLMBIcited by 14
  11. 2024Risks and Opportunities of Open-Source Generative AIcited by 11
  12. 2021Simulated Adversarial Testing of Face Recognition Modelscited by 10
  13. 2024The Necessity of AI Audit Standards Boardscited by 9
  14. 2024Adapting cybersecurity frameworks to manage frontier AI risks: A defense-in-depth approachcited by 4
  15. 2024Art and Science of Quantizing Large-Scale Models: A Comprehensive Overviewcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphscited by 17
  2. 2026Intelligent AI Delegationcited by 16
  3. 2025Distributional AGI Safetycited by 8
  4. 2026AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainabilitycited by 0
  5. 2026Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoptioncited by 0