Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence

TL;DR

This paper proposes a comprehensive framework to understand and evaluate openness in foundation models across the AI stack, aiming to facilitate nuanced discussions on openness and safety in AI development.

Contribution

It introduces a detailed framework for assessing openness in foundation models, addressing the complexity and variability of openness across different AI components.

Findings

Summarizes previous work on openness in AI.

Analyzes reasons for pursuing openness in foundation models.

Outlines how openness varies across the AI stack.

Abstract

Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level of generality or with a narrow focus on specific technical attributes. In part, this is because defining open source for foundation models has proven tricky, given its significant differences from traditional software development. In order to inform more practical and nuanced decisions about opening AI systems, including foundation models, this paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness, and outlines how openness varies in different parts of the AI stack, both at the model and at the system level. In doing so, its authors hope to provide a common descriptive framework to deepen a nuanced and rigorous understanding of openness in AI and enable further work around definitions of openness and safety in AI.