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的發佈決策決定了系統組件是否對外開放，但發佈本身並未觸及許多其他影響用戶和利益相關者如何與系統互動的要素。在發佈之外，對系統組件的接觸程度揭示了潛在的風險與收益。所謂接觸，指的是在實踐層面，無論是基礎設施、技術還是社會層面，為了以某種方式使用可用組件所需滿足的條件。我們從三個維度解構接觸：資源配置、技術可用性及實用性。在每個類別中，針對每個系統組件的一系列變量闡明了權衡點。例如，資源配置需要接觸到計算基礎設施以提供模型權重。我們還比較了四種高性能語言模型的接觸難易度，其中兩種為開放權重，兩種為封閉權重，顯示出基於接觸變量，所有模型都有相似的考量因素。接觸變量為擴大或增加用戶接觸奠定了基礎；我們探討了接觸的規模以及規模如何影響管理和干預風險的能力。這一框架更全面地涵蓋了系統發佈的全局及風險收益權衡，為系統發佈決策、研究及政策制定提供了參考。

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.