Ontology of Belief Diversity: A Community-Based Epistemological Approach

TL;DR

This paper presents a community-based epistemological approach to constructing an inclusive ontology of belief systems, validated through user studies and sentiment analysis in language models.

Contribution

It introduces a pragmatic, community-driven methodology for developing belief ontologies that respect diversity and inclusivity in social concepts.

Findings

Epistemological categorization effectively captures belief differences.

Community consensus enhances ontology inclusivity.

Method improves belief fairness in language models.

Abstract

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.