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Beyond Release: Access Considerations for Generative AI Systems

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

TL;DR

This paper argues that evaluating generative AI deployments requires more than release status; it introduces an access-centric framework with three axes—resourcing, technical usability, and utility—to analyze how people can actually engage with model components. By comparing four high-profile language models (two open-weight and two closed-weight), it demonstrates that access variables—rather than openness alone—drive the real-world risks and benefits, including scalability, monitoring, and intervention capabilities. The work highlights concrete tradeoffs across hardware, software, data, licensing, and interfaces, and discusses governance, distribution, and time-evolving factors that affect deployment decisions. Overall, the framework aims to guide researchers, policymakers, and practitioners in making more informed release and access decisions that balance benefit, risk, and equity in rapidly evolving AI ecosystems.

Abstract

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.

Beyond Release: Access Considerations for Generative AI Systems

TL;DR

This paper argues that evaluating generative AI deployments requires more than release status; it introduces an access-centric framework with three axes—resourcing, technical usability, and utility—to analyze how people can actually engage with model components. By comparing four high-profile language models (two open-weight and two closed-weight), it demonstrates that access variables—rather than openness alone—drive the real-world risks and benefits, including scalability, monitoring, and intervention capabilities. The work highlights concrete tradeoffs across hardware, software, data, licensing, and interfaces, and discusses governance, distribution, and time-evolving factors that affect deployment decisions. Overall, the framework aims to guide researchers, policymakers, and practitioners in making more informed release and access decisions that balance benefit, risk, and equity in rapidly evolving AI ecosystems.

Abstract

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.

Paper Structure

This paper contains 35 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Previous and Related Work
  3. From Available to Accessed
  4. Breaking Down Access
  5. Resourcing
  6. Model
  7. Code
  8. Training Data
  9. Comparing models
  10. Technical Usability
  11. Model
  12. Code
  13. Training Data
  14. License
  15. Comparing models
  16. ...and 20 more sections

Figures (4)

  • Figure 1: From Available to Accessible with High Level Tradeoffs
  • Figure 2: Categories of Access and Respective Variables
  • Figure 3: Flow of Access and Scale with High Level Tradeoffs
  • Figure 4: Shifting from Available to Accessible with High Level Tradeoffs