超越发布：生成式AI系统的访问考量

Beyond Release: Access Considerations for Generative AI Systems

February 23, 2025

作者: Irene Solaiman, Rishi Bommasani, Dan Hendrycks, Ariel Herbert-Voss, Yacine Jernite, Aviya Skowron, Andrew Trask

cs.AI

摘要

生成式AI的发布决策决定了系统组件是否对外开放，但发布本身并未解决许多其他影响用户及利益相关方与系统互动的要素。在发布之外，系统组件的可访问性直接关系到潜在风险与收益。这里的“访问”指的是从资源、技术和社会层面满足实际需求，以便以某种方式利用已发布的组件。我们将访问性分解为三个维度：资源配置、技术可用性和实用性。在每个类别中，针对每个系统组件的一系列变量阐明了其中的权衡。例如，资源配置要求具备访问计算基础设施的能力以提供模型权重。我们还对比了四种高性能语言模型的可访问性，其中两种为开放权重，两种为封闭权重，展示了基于访问变量的相似考量因素。访问变量为扩大或提升用户访问能力奠定了基础；我们探讨了访问的规模以及规模如何影响风险管理和干预的能力。这一框架更全面地涵盖了系统发布的全貌及风险与收益的权衡，为系统发布决策、研究及政策制定提供了更深入的洞见。

English

Generative AI release decisions determine whether system components are made available, but release does not address many other elements that change how users and stakeholders are able to engage with a system. Beyond release, access to system components informs potential risks and benefits. Access refers to practical needs, infrastructurally, technically, and societally, in order to use available components in some way. We deconstruct access along three axes: resourcing, technical usability, and utility. Within each category, a set of variables per system component clarify tradeoffs. For example, resourcing requires access to computing infrastructure to serve model weights. We also compare the accessibility of four high performance language models, two open-weight and two closed-weight, showing similar considerations for all based instead on access variables. Access variables set the foundation for being able to scale or increase access to users; we examine the scale of access and how scale affects ability to manage and intervene on risks. This framework better encompasses the landscape and risk-benefit tradeoffs of system releases to inform system release decisions, research, and policy.