mlsanityData to 7 Oct 2026

Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries

arXiv 2311.12573 ↗First posted 21 Nov 2023cs.CY cs.AI cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

The AI development community is increasingly making use of hosting intermediaries such as Hugging Face provide easy access to user-uploaded models and training data. These model marketplaces lower technical deployment barriers for hundreds of thousands of users, yet can be used in numerous potentially harmful and illegal ways. In this article, we explain ways in which AI systems, which can both `contain' content and be open-ended tools, present one of the trickiest platform governance challenges seen to date. We provide case studies of several incidents across three illustrative platforms -- Hugging Face, GitHub and Civitai -- to examine how model marketplaces moderate models. Building on this analysis, we outline important (and yet nevertheless limited) practices that industry has been developing to respond to moderation demands: licensing, access and use restrictions, automated content moderation, and open policy development. While the policy challenge at hand is a considerable one, we conclude with some ideas as to how platforms could better mobilize resources to act as a careful, fair, and proportionate regulatory access point.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 20 of its 119 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

20 of 119 references.

References · 20

  1. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  4. 2023Who's Harry Potter? Approximate Unlearning in LLMscited by 366
  5. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  6. 2023Stable Bias: Analyzing Societal Representations in Diffusion Modelscited by 225
  7. 2021Challenges in Detoxifying Language Modelscited by 169
  8. 2022A Review on Language Models as Knowledge Basescited by 156
  9. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 126
  10. 2023Regulating ChatGPT and other Large Generative AI Modelscited by 102
  11. 2021Selective Differential Privacy for Language Modelingcited by 61
  12. 2018Algorithms that Remember: Model Inversion Attacks and Data Protection Lawcited by 52
8 more
  1. 2023LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?cited by 47
  2. 2020Behavioral Use Licensing for Responsible AIcited by 46
  3. 2023Understanding accountability in algorithmic supply chainscited by 39
  4. 2020Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Boardcited by 24
  5. 2023Safety and Fairness for Content Moderation in Generative Modelscited by 15
  6. 2023Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in MLcited by 9
  7. 2021Understanding the Complexity of Detecting Political Adscited by 2
  8. 2021Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysiscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.