  AI applications across classification, fairness, and human interaction often
implicitly require ontologies of social concepts. Constructing these well,
especially when there are many relevant categories, is a controversial task but
is crucial for achieving meaningful inclusivity. Here, we focus on developing a
pragmatic ontology of belief systems, which is a complex and often
controversial space. By iterating on our community-based design until mutual
agreement is reached, we found that epistemological methods were best for
categorizing the fundamental ways beliefs differ, maximally respecting our
principles of inclusivity and brevity. We demonstrate our methodology's utility
and interpretability via user studies in term annotation and sentiment analysis
experiments for belief fairness in language models.
