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, 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：案例研究通常是法学、伦理学以及许多其他领域中教学的基础，这些领域面临着人类价值观所影响的复杂且模糊的社会问题。当我们思考AI在实践中应如何对齐时，也会出现类似的复杂性和模糊性：当面对来自不同个体和社区的海量多样化（有时相互冲突）价值观时，AI应与谁的价值观对齐，以及应如何对齐？我们提出了一种基于案例推理（CBR）思想的宪法AI对齐的补充方法，该方法侧重于通过对一组案例的评判来构建策略。我们提出了一个构建此类案例库的流程：1）在特定领域中收集一组“种子”案例——即人们可能向AI系统提出的问题；2）通过与领域专家的研讨会，提炼出特定领域的关键维度；3）利用LLMs生成未见过的案例变体；4）让公众参与评判和改进案例。然后，我们讨论了这样的案例库如何能够辅助AI对齐，既可以直接作为先例来约束可接受的行为，也可以作为个体和社区围绕AI进行道德推理的媒介。