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权限类型及其授予对象。尽管现有安全框架和AI开发者使用政策已认识到特定模型的风险与其向特定受众提供的访问级别相关，但其用于制定模型访问决策的程序仍存在临时性、不透明性及缺乏实证依据的问题。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) 针对不同访问方式实证评估模型能力的流程；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先承诺。