Artificial Intelligence As Topic and Tool In Stratification Research

Artificial intelligence is changing how stratification scholars in and beyond RC28 can measure and model inequality. This session features work that uses advanced computational methods to integrate AI and machine learning into the empirical study of inequality, as well as work that examines how advanced technological systems themselves produce stratified outcomes. Topics include but are not restricted to: survey methodology using language models for coding open-ended responses; audit studies, field experiments, and conjoint survey experiments that use conversational agents or algorithmically generated stimuli to measure discrimination; geospatial work combining satellite imagery, mobility data, and administrative records to study neighborhood effects and spatial inequalities; agent-based and generative simulations examining processes such as residential segregation, cumulative advantage, or intergenerational mobility; population-scale analysis of large-scale text from interviews, field notes, open-ended survey responses, and historical records; and studies of algorithmic hiring, credit scoring, healthcare triage, and content moderation as mechanisms that generate or reproduce contemporary inequality.