论文提出评估生成式AI系统访问性的三维框架，超越单纯发布模型。
Beyond Release: Access Considerations for Generative AI Systems
- 从资源、技术可用性、实用性三方面拆解系统访问问题
- 对比四款大模型发现，开放权重不等于易用，访问变量决定实际可用性
- 为研究、政策制定提供可量化的风险收益权衡工具
生成式AI的发布决策仅涉及组件是否公开，但发布并未解决用户与利益相关方如何实际使用系统的问题。真正的使用依赖于对系统组件的访问能力，涵盖基础设施、技术和社会层面的实际需求。本文将访问性分解为三个维度：资源获取、技术可用性和功能效用，并在每个维度下定义具体变量以揭示各组件间的权衡。例如，资源获取要求具备运行模型权重所需的计算基础设施。通过对比两款开源权重与两款闭源权重的高性能语言模型，发现所有模型均面临相似的访问挑战，核心差异在于访问变量而非开放程度。这些变量构成了扩大用户覆盖范围的基础，本文进一步分析了访问规模对风险管控与干预能力的影响。该框架更全面地呈现系统发布的整体图景与风险收益平衡，有助于指导系统发布决策、学术研究与政策制定。
原文摘要 · Abstract (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.
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