Artificial intelligence (AI) systems will increasingly be used to cause harm as they grow more capable. In fact, AI systems are already starting to be used to automate fraudulent activities, violate human rights, create harmful fake images, and identify dangerous toxins. To prevent some misuses of AI, we argue that targeted interventions on certain capabilities will be warranted. These restrictions may include controlling who can access certain types of AI models, what they can be used for, whether outputs are filtered or can be traced back to their user, and the resources needed to develop them. We also contend that some restrictions on non-AI capabilities needed to cause harm will be required. Though capability restrictions risk reducing use more than misuse (facing an unfavorable Misuse-Use Tradeoff), we argue that interventions on capabilities are warranted when other interventions are insufficient, the potential harm from misuse is high, and there are targeted ways to intervene on capabilities. We provide a taxonomy of interventions that can reduce AI misuse, focusing on the specific steps required for a misuse to cause harm (the Misuse Chain), and a framework to determine if an intervention is warranted. We apply this reasoning to three examples: predicting novel toxins, creating harmful images, and automating spear phishing campaigns.

翻译：人工智能系统随着能力的增强，将越来越多地被用于造成伤害。事实上，人工智能系统已经开始被用于自动化欺诈活动、侵犯人权、制作有害的虚假图像以及识别危险毒素。为了防止某些人工智能的滥用，我们认为有必要对某些能力进行有针对性的干预。这些限制包括控制谁可以访问特定类型的人工智能模型、它们可用于何种用途、输出是否经过过滤或可追溯到用户，以及开发它们所需的资源。我们还主张，对造成伤害所需的非人工智能能力也需施加一些限制。尽管能力限制可能更多减少使用而非滥用（面临不利的滥用-使用权衡），但我们认为，当其他干预措施不足、滥用可能造成的潜在危害很大，并且存在针对性的能力干预方式时，对能力进行干预是正当的。我们提出了一份可减少人工智能滥用的干预措施分类法，重点关注滥用造成伤害所需的具体步骤（滥用链），以及一个判断干预是否正当的框架。我们将这一推理应用于三个例子：预测新型毒素、制作有害图像以及自动化鱼叉式网络钓鱼活动。