It is argued that effective governance of OWFMs requires a multi-layered approach integrating three complementary components: model cards, acceptable use policies (AUPs), and licenses, and that standard open-source licenses are not well suited for OWFMs and may weaken the enforceability of AUPs.
Abstract
The growth of open-weight foundation models (OWFMs) has prompted the AI community to re-evaluate strategies for effective downstream governance. Although model cards have been widely adopted as transparency artifacts in model repositories, existing frameworks often fail to adequately inform downstream developers and users about the distinct safety challenges posed by OWFMs. This position paper analyzes 500 model cards hosted on Hugging Face and argues that effective governance of OWFMs requires a multi-layered approach integrating three complementary components: (i) model cards, (ii) acceptable use policies (AUPs), and (iii) licenses. To motivate this claim, we identify a safety gap left by existing regulatory approaches, including model heritage, alignment provenance, and empirically observed behaviors, through an analysis of model cards with safety-critical information. We further argue that standard open-source licenses (OSLs) are not well suited for OWFMs and may weaken the enforceability of AUPs. Building on these observations, we outline directions for evolving model cards, AUPs, and licenses into integrated safety artifacts to enable a more comprehensive governance framework that coherently integrates informational, normative, and legal dimensions.
This paper presents MDI’s Data Governance Transformation (DGT) project, an already-in-use data governance policy & infrastructure framework developed to support robust implementation of Privacy Enhancing Technologies (PETs), govern rapid AI development, and confront precedent shattering data use in the United States. As government agencies increasingly rely on complex and distributed data ecosystems, traditional data management approaches have proven insufficient to ensure data quality, accessibility, privacy, and interoperability. This framework establishes a cross-functional data governance structure that includes clearly defined roles, a RACI (Responsible, Accountable, Consulted, Informed) matrix, a standardized change control process, and an architecture rooted in medallion-style data layering. It also includes practical guidance on the following: conducting a data inventory, aligning with cloud and privacy requirements, and coordinating with contractors to ensure data portability and reproducibility. By embedding PETs and clear accountability mechanisms, this framework not only supports compliance with regulatory mandates but also enables data-driven decision-making and responsible use of AI. The framework serves as a replicable model for other government and non-government entities seeking to implement or refresh their data governance strategies to meet the demands of modern public service delivery.
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