The downstream use cases, benefits, and risks of AI models depend significantly on what sort of access is provided to the model, and who it is provided to. Though existing safety frameworks and AI developer usage policies recognise that the risk posed by a given model depends on the level of access provided to a given audience, the procedures they use to make decisions about model access are ad hoc, opaque, and lacking in empirical substantiation. This paper consequently proposes that frontier AI companies build on existing safety frameworks by outlining transparent procedures for making decisions about model access, which we term Responsible Access Policies (RAPs). We recommend that, at a minimum, RAPs should include the following: i) processes for empirically evaluating model capabilities given different styles of access, ii) processes for assessing the risk profiles of different categories of user, and iii) clear and robust pre-commitments regarding when to grant or revoke specific types of access for particular groups under specified conditions.

翻译：人工智能模型的下游应用场景、效益及风险在很大程度上取决于模型所提供的访问类型及访问对象。尽管现有安全框架与AI开发者使用政策已认识到特定模型的风险水平取决于向特定受众提供的访问权限，但其用于制定模型访问决策的程序往往具有临时性、不透明性且缺乏实证依据。为此，本文建议前沿人工智能企业应在现有安全框架基础上，构建透明的模型访问决策程序，即"负责任访问政策"。我们建议该政策至少应包含以下要素：i) 针对不同访问模式对模型能力进行实证评估的流程；ii) 评估不同用户类别风险特征的流程；iii) 关于在特定条件下何时向特定群体授予或撤销特定访问权限的明确且可靠的事前承诺。