Ontology of Belief Diversity: A Community-Based Epistemological Approach
This work addresses the need for better ontologies of social concepts in AI, particularly for fairness and inclusivity, but is incremental as it builds on existing community-based and epistemological approaches.
The paper tackled the problem of constructing a pragmatic ontology for belief systems to improve inclusivity in AI applications, and demonstrated its utility through user studies in term annotation and sentiment analysis experiments for belief fairness in language models.
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.