Beyond Release: Access Considerations for Generative AI Systems

TL;DR

This paper introduces a framework for understanding access to generative AI systems beyond their release, focusing on resourcing, usability, and utility to inform risk management and policy decisions.

Contribution

It deconstructs access into three axes with variables, providing a comprehensive framework to evaluate and compare system accessibility and associated risks.

Findings

Access variables influence scalability and risk management.

Comparison of four language models shows similar access considerations.

Framework helps inform release, research, and policy decisions.

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.