Open foundation models like BERT, CLIP, and Stable Diffusion signify a new era in the technology space, particularly in artificial intelligence (AI). They provide free access to model weights, enhancing customization, and accessibility. While this development brings benefits to innovation and research, it also introduces fresh risks and potential misuse, which has initiated a critical debate around the open versus closed release of these foundation models around the globe.
Traditionally, AI development integrated methods with closed foundation models, in which model weights are inaccessible to the public. This limited the ability of researchers and developers to inspect or customize these models. Open foundation models challenge this approach by providing an alternative that encourages innovation, competition, and transparency. However, this openness also introduces a new set of challenges regarding misuse as these models, once released, cannot be controlled or their use monitored.
Open foundation models offer numerous advantages, including boosting innovation and speeding up scientific research. By enabling more expansive access and customization, these models distribute decision-making power, thus allowing a range of applications to be tailored per specific needs. Additionally, they play a significant role in AI research by offering essential tools for exploring AI interpretability, security, and safety. On the flip side, they present some downsides, such as comparative disadvantages in model improvement over time due to inadequate user feedback.
Despite their immense advantages, the risks associated with open foundation models cannot be ignored. Society could be exposed to harm through misuse in areas like cybersecurity, biosecurity, and non-consensual intimate imagery generation. Hence, an analytical framework to understand the nature of these risks, including the threat identification, existing risks, defenses, evidence of marginal risk, and the measures to defend against new risks and underlying uncertainties and assumptions, is advocated.
In conclusion, open foundation models are causing a shift in the AI sector, providing substantial benefits and simultaneously introducing new risks. The impact on innovation, transparency, and scientific research is profound. However, it is also crucial to consider the significant risks they pose and deem necessary governance. A balanced approach, guided by empirical evidence and an in-depth understanding of these models’ unique properties, is necessary to maximize their potential while controlling their risks. The AI community and policymakers must navigate these new territories judiciously.
