Case studies commonly form the pedagogical backbone in law, ethics, and many other domains that face complex and ambiguous societal questions informed by human values. Similar complexities and ambiguities arise when we consider how AI should be aligned in practice: when faced with vast quantities of diverse (and sometimes conflicting) values from different individuals and communities, with whose values is AI to align, and how should AI do so? We propose a complementary approach to constitutional AI alignment, grounded in ideas from case-based reasoning (CBR), that focuses on the construction of policies through judgments on a set of cases. We present a process to assemble such a case repository by: 1) gathering a set of ``seed'' cases -- questions one may ask an AI system -- in a particular domain from discussions in online communities, 2) eliciting domain-specific key dimensions for cases through workshops with domain experts, 3) using LLMs to generate variations of cases not seen in the wild, and 4) engaging with the public to judge and improve cases. We then discuss how such a case repository could assist in AI alignment, both through directly acting as precedents to ground acceptable behaviors, and as a medium for individuals and communities to engage in moral reasoning around AI

翻译：案例研究通常是法律、伦理学及其他面临复杂模糊社会问题（这些问题的解答需基于人类价值观）领域教学的基础。当思考AI在实践中应如何对齐时，类似的复杂性与模糊性也随之显现：面对不同个体与社群提出的海量多样化（且有时相互冲突）价值观时，AI应当与谁的价值观对齐，又该如何实现对齐？我们提出一种基于案例推理思想的宪法AI对齐补充方法，该方法聚焦于通过案例集的判断来构建政策。我们通过以下流程构建此类案例库：1）从在线社区的讨论中收集特定领域的"种子"案例（即人们可能向AI系统提出的问题）；2）通过与领域专家举办研讨会，提取案例的领域特定关键维度；3）利用大语言模型生成未在真实场景中出现过的案例变体；4）邀请公众参与对案例的判断与优化。随后，我们讨论此类案例库如何通过直接作为规范可接受行为的前例，以及作为个体与社群围绕AI进行道德推理的媒介，两种途径助力AI对齐。