Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries

TL;DR

This paper examines the governance challenges faced by AI model marketplaces, analyzing case studies and current moderation practices to suggest ways platforms can improve regulation and safety.

Contribution

It provides a detailed analysis of moderation practices and governance issues in AI model marketplaces through case studies and proposes strategies for better platform regulation.

Findings

Case studies of moderation on Hugging Face, GitHub, and Civitai

Current moderation practices include licensing and automated content filtering

Platforms need to develop more effective and fair governance mechanisms

Abstract

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.