  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.
