On the Societal Impact of Open Foundation Models (2403.07918v1)

Abstract: Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.

• The paper introduces a framework that assesses both benefits and risks of open foundation models.
• It identifies five key properties, including broader access, customizability, local inference, irrevocable access, and weak monitoring.
• The authors offer recommendations for AI developers, risk researchers, and policymakers to balance innovation and safety.

On the Societal Impact of Open Foundation Models

The paper "On the Societal Impact of Open Foundation Models" examines the implications of open foundation models whose weights are publicly available, such as Llama 2 and Stable Diffusion XL. The authors categorize the societal consequences into benefits and risks, attempting to strike a balance between them through a nuanced framework.

Distinctive Properties

Five distinctive properties of open foundation models are identified:

1. Broader Access: These models are characterized by the wide availability of weights, facilitating greater inclusivity in usage.
2. Greater Customizability: Users can modify these models for specific applications due to open access to weights.
3. Local Adaptation and Inference: These models can run locally, circumventing the necessity to share data with external servers.
4. Irrevocability of Access: Once released, access to model weights cannot be revoked, raising potential misuse concerns.
5. Weaker Monitoring: Monitoring usage is challenging, especially for local instances.

Benefits

The paper articulates several benefits derived from these properties:

• Distributing Decision-Making Power: Open models allow for a more decentralized determination of acceptable behavior, diversifying perspectives and applications.
• Innovation and Competition: The accessibility of open models accelerates new use cases and market innovation, though this must be weighed against the potential for model fragmentation and the comparative advantages of closed models in improvement and feedback.
• Scientific Research: Open models enhance reproducibility and inclusion in research, though the complete benefits hinge on access to data and other assets beyond weights.
• Transparency: Open models permit greater scrutiny and accountability, particularly pertinent given historic opacity in digital technologies.
• Mitigating Market Concentration: By enabling smaller entities to compete, open models could distribute market power more evenly.

Risks

The authors provide a framework to assess the societal risks of open foundation models, emphasizing the marginal risk:

• Misuse Vectors: The risks are articulated across vectors such as cybersecurity, biosecurity, disinformation, and other forms of social harm.
• Marginal Risk Framework: This framework assesses whether these models increase societal risk relative to pre-existing technologies or closed models and considers the existing defenses and their potential efficacy against newly introduced risks.

Evidence and Analysis

The paper's strength lies in its structured approach to risk assessment, although it acknowledges the current insufficiency of data in several areas. The framework aims to stimulate precise discourse on the marginal risks of open models, contrasting with pre-existing risks from other technologies.

Recommendations

The paper concludes with targeted recommendations:

• AI Developers: Transparency and clear delineation of responsibility for safe and responsible use are critical.
• Risk Researchers: Further empirical work is needed to elucidate the nuanced balance of risks and benefits.
• Policymakers and Regulators: The authors suggest thoughtful regulation that considers the unique challenges posed by open models, without stifling competition or innovation.

Conclusion

This comprehensive examination provides a rational basis for ongoing debates on open foundation models, emphasizing the need for collaborative efforts among developers, researchers, and policymakers to harness their benefits while mitigating associated risks. The research implies a call to action for empirical studies to inform future AI governance and deployment strategies.