Skip to main navigation menu Skip to main content Skip to site footer

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN ASSESSING REGIONAL SOCIAL STRATIFICATION

Affiliations
a Xalqaro Nordik universiteti image/svg+xml
b Toshkent Davlat Iqtisodiyot Universiteti image/svg+xml

Annotatsiya

This article analyzes, from an economic-sociological perspective, a conceptual platform designed to identify and monitor the level of social stratification across regions using artificial intelligence (AI) technologies. The study develops a set of indicators for measuring regional stratification based on a synthesis of existing literature, defines principles of data integration and anonymization, and proposes the RISI (Regional Social Stratification Index). Pilot tests calculated the RISI for several regions using administrative and survey data, identifying regions with high, medium, and low levels of stratification. SHAP interpretation shows that income dispersion, educational attainment, internet speed, and the digital skills index are the most influential factors affecting the RISI. Clustering results and anomaly detection confirm the practical value of the platform in identifying regional disparities and directing policy interventions more effectively.

References

  1. DiMaggio, P., Hargittai, E., Neuman, W. R., & Robinson, J. P. (2001). Social implications of the Internet. Annual Review of Sociology, 27(1), 307–336
  2. Matamoros-Fernández, A. (2017). Platformed racism: The mediation and circulation of an Australian race-based controversy on Twitter, Facebook and YouTube. Information, Communication & Society, 20(6), 930–946
  3. Blank, C. U., Rozeman, E. A., Fanchi, L. F., Sikorska, K., van de Wiel, B. A., Kvistborg, P., van Thienen, J. V., van den Braber, M., van der Hiel, B., de Vos, L., van Meerten, T., & Schadendorf, D. (2018). Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma. Nature Medicine, 24(11), 1655–1661
  4. Hargittai, E., Piper, A. M., & Morris, M. R. (2019). From internet access to internet skills: Digital inequality among older adults. Universal Access in the Information Society, 18(4), 881–890
  5. Bacher‐Hicks, A., Goodman, J., & Mulhern, C. (2021). Inequality in household adaptation to schooling shocks: COVID‐induced online learning engagement in real time. Journal of Public Economics, 193, 104345. https://doi.org/10.1016/j.jpubeco.2020.104345
  6. DiMaggio, P., Hargittai, E., Celeste, C., & Shafer, S. (2004). Digital inequality: From unequal access to differentiated use. In D. B. Grusky (Ed.), Social stratification: Class, race, and gender in sociological perspective (pp. 355–400). Westview Press

Yuklab olishlar

Yuklab olish ma’lumotlari hali mavjud emas.