The advent of open foundation models, such as BERT, CLIP, and Stable Diffusion, has ushered in a new era in artificial intelligence, marked by rapid technological development and significant societal impact. These models are characterized by their widely available model weights, allowing for greater customization and broader access, which, in turn, offers a host of benefits and introduces new risks. This evolution has sparked a critical debate on the open versus closed release of foundation models, with significant attention from policymakers globally.
Current state-of-the-art methods in AI development often involve closed foundation models, where model weights are not publicly available, limiting the ability of researchers and developers to customize or inspect these models. Open foundation models challenge this paradigm by offering an alternative that promotes innovation, competition, and transparency. These models enable local adaptation and inference, making them particularly valuable in fields where data sensitivity is paramount. However, their open nature also means once released, controlling access or use becomes nearly impossible, raising concerns about misuse and the difficulty of moderating or monitoring their application.
The benefits of open foundation models are significant, spanning from fostering innovation and accelerating scientific research to enhancing transparency and reducing market concentration. By allowing broader access and customization, these models distribute decision-making power regarding acceptable model behavior, enabling a diversity of applications that can be tailored to specific needs. They also play a crucial role in scientific research by providing essential tools for exploration in AI interpretability, security, and safety. However, these advantages come with caveats, such as potential comparative disadvantages in model improvement over time due to the lack of user feedback and the fragmented use of heavily customized models.
Despite these benefits, open foundation models present risks, especially in terms of societal harm through misuse in areas like cybersecurity, biosecurity, and the generation of non-consensual intimate imagery. To understand the nature of these risks, this study presents a framework that centers marginal risk: what additional risk is society subject to because of open foundation models relative to pre-existing technologies, closed models, or other relevant reference points? This framework considers the threat identification, existing risks, defenses, evidence of marginal risk, ease of defending against new risks, and the underlying uncertainties and assumptions. It highlights the importance of a nuanced approach to evaluating the risks and benefits of open foundation models, underscoring the need for empirical research to validate theoretical benefits and risks.
In conclusion, open foundation models represent a pivotal shift in the AI landscape, offering substantial benefits while posing new challenges. Their impact on innovation, transparency, and scientific research is undeniable, yet they also introduce significant risks that require careful consideration and governance. As the AI community and policymakers navigate these waters, a balanced approach, informed by empirical evidence and a deep understanding of the distinctive properties of open foundation models, will be essential for harnessing their potential while mitigating their risks.
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