Year: 2024
Type: conference-paper
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 isa 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
Authors: Richard Zhang, Erin Van Liemt, Tyler Fischella
Institutions: Google (United States), Google DeepMind (United Kingdom)
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
OpenAlex ID: W4404518174