Assessing Historical Structural Oppression Worldwide via Rule-Guided Prompting of Large Language Models
Chatterjee, Sreejato, Tran, Linh, Nguyen, Quoc Duy, Kirson, Roni, Hamlin, Drue, Aquino, Harvest, Lyu, Hanjia, Luo, Jiebo, Dye, Timothy
–arXiv.org Artificial Intelligence
Abstract--Traditional efforts to measure historical structural oppression struggle with cross-national validity due to the unique, locally specified histories of exclusion, colonization, and social status in each country, and often have relied on structured indices that privilege material resources while overlooking lived, identity-based exclusion. We introduce a novel framework for oppression measurement that leverages Large Language Models (LLMs) to generate context-sensitive scores of lived historical disadvantage across diverse geopolitical settings. Using unstructured self-identified ethnicity utterances from a multilingual COVID-19 global study, we design rule-guided prompting strategies that encourage models to produce interpretable, theoretically grounded estimations of oppression. We systematically evaluate these strategies across multiple state-of-the-art LLMs. Our results demonstrate that LLMs, when guided by explicit rules, can capture nuanced forms of identity-based historical oppression within nations. This approach provides a complementary measurement tool that highlights dimensions of systemic exclusion, offering a scalable, cross-cultural lens for understanding how oppression manifests in data-driven research and public health contexts. The study of racial and ethnic inequality remains central to sociological research, with extensive research documenting how structural oppression is reproduced in historical and contemporary contexts [1]-[3]. Oppression can be understood as a social hierarchy in which some groups subject other groups to lower status and to systemic exclusion, dehumanization, and disadvantage. In public health and sociology, this oppression is closely aligned with definitions of systemic and structural racism, which describe racism as deeply embedded in laws, policies, institutional practices, and social norms that sustain widespread inequities, violence, and disadvantage over time [1]. Foundational works have demonstrated how ethnic and national hierarchies shape access to power, life opportunities, autonomy, and sovereignty, for example, primarily through institutionalized mechanisms such as legal structures, educational systems, and healthcare access, among others [2].
arXiv.org Artificial Intelligence
Nov-25-2025
- Country:
- Africa
- Madagascar (0.05)
- Middle East > Algeria (0.05)
- Asia
- India (0.04)
- Middle East > Palestine (0.04)
- Europe
- Finland > Uusimaa
- Helsinki (0.04)
- Middle East (0.04)
- Sweden (0.04)
- United Kingdom (0.04)
- Finland > Uusimaa
- North America
- Canada (0.04)
- Central America (0.04)
- Puerto Rico (0.04)
- United States
- Alaska (0.04)
- New Jersey > Mercer County
- Princeton (0.04)
- New York > Monroe County
- Rochester (0.05)
- Oceania > Australia (0.05)
- South America > Brazil (0.05)
- Africa
- Genre:
- Research Report > New Finding (1.00)
- Industry:
- Technology: