The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence (2403.13784v6)

Abstract: Generative AI offers numerous opportunities for research and innovation, but its commercialization has raised concerns about the transparency and safety of frontier AI models. Most models lack the necessary components for full understanding, auditing, and reproducibility, and some model producers use restrictive licenses whilst claiming that their models are "open source". To address these concerns, we introduce the Model Openness Framework (MOF), a three-tiered ranked classification system that rates machine learning models based on their completeness and openness, following open science principles. For each MOF class, we specify code, data, and documentation components of the model development lifecycle that must be released and under which open licenses. In addition, the Model Openness Tool (MOT) provides a user-friendly reference implementation to evaluate the openness and completeness of models against the MOF classification system. Together, the MOF and MOT provide timely practical guidance for (i) model producers to enhance the openness and completeness of their publicly-released models, and (ii) model consumers to identify open models and their constituent components that can be permissively used, studied, modified, and redistributed. Through the MOF, we seek to establish completeness and openness as core tenets of responsible AI research and development, and to promote best practices in the burgeoning open AI ecosystem.

Promoting Model Transparency with the Model Openness Framework

Introduction to the Model Openness Framework (MOF)

The Model Openness Framework (MOF) emerges as a structured proposal to address prevalent issues surrounding the reproducibility, transparency, and usability of AI systems, particularly in the context of Generative AI (GAI). It introduces a classification system designed to evaluate and promote the open sharing of machine learning model components, embracing the principles of open science across its lifecycle. The MOF advocates for the inclusion of specific components and their release under permissive open licenses, setting a criterion that distinguishes genuinely open models from those merely labeled as "open-source" under the guise of "open washing". This framework is poised to offer a systematic approach to ensuring models presented as open source adhere to the ethos of openness, offering clarity and direction for both model producers and consumers.

Delving into Completeness and Openness

The MOF distinguishes between "completeness" and "openness" as two pivotal dimensions of model sharing. Completeness refers to the extent to which all essential artifacts resulting from the model development process are shared, including but not limited to datasets, code, and documentation. Openness, on the other hand, encapsulates the licensing aspect, ensuring each component is shared under licenses that allow for free access, modification, and redistribution. The nuanced distinction serves to underscore a model's transparency and its alignment with open science ideals, facilitating a comprehensive understanding and utilization by the broader research and development community.

Structuring the Framework: From Open Models to Open Science

The MOF framework classifies models into three ascending tiers based on their level of completeness and openness. Class III represents the basic level where the model and minimal documentation are shared under open licenses. Class II, 'Open Tooling', extends to include all necessary code for training and evaluation, promoting a deeper level of engagement with the model. Class I, 'Open Science', epitomizes the ideal, including exhaustive datasets, detailed documentation, and interim model states to allow for full reproducibility and scrutiny. This tiered approach encourages progressive strides towards complete openness, advocating for a shared standard that resonates with the open science movement.

MOF's Practical Implementation

The introduction of a practical implementation process and a badging system underpins the MOF's commitment to actionable transparency. Through self-assessment or the utilization of the MOF's tooling, model producers can classify their work, receiving a badge indicative of their level of openness. This system not only serves as a metric of transparency but also as a motivator for the adoption of open science practices in AI development, ensuring a clear, user-verifiable standard of openness.

Forward-looking Implications and Speculations

By setting a clear standard for openness in AI, the MOF framework is positioned to significantly impact future developments in the field. It could catalyze a shift towards more transparent, reproducible AI research, fostering an environment where trust in AI systems is built on verifiable openness and where innovation is spurred by the unencumbered sharing of knowledge and resources. Moreover, the widespread adoption of this framework might streamline compliance with emerging regulations on AI transparency and ethical AI development practices, contributing to the global effort to establish trustworthy AI.

Conclusion: A Call to Embrace Openness in AI

The MOF introduces a formalized, actionable pathway to foster openness in AI, drawing a definitive line between truly open models and those that merely claim the title. By adopting and advocating for the standards set forth by the MOF, the AI community can push towards an era of enhanced transparency, accelerated innovation, and collective progress. The integration of this framework across AI development practices promises not only to elevate the field's ethical standards but also to deepen the trust of the public and stakeholders in AI technologies.