The rapid development of artificial intelligence (AI) technologies has significantly transformed how social scientists and policymakers understand, model, and forecast societal change. AI is not only a computational tool but also a catalyst for rethinking the dynamics of social systems, enabling the prediction of emerging behavioral patterns, the identification of hidden regularities, and the modeling of complex interactions across various levels of society. This paper explores the epistemological and methodological implications of using artificial intelligence in designing and forecasting social dynamics. Drawing on interdisciplinary approaches from the philosophy of science, systems theory, and digital sociology, the study examines how machine learning algorithms, agent-based models, and big data analytics contribute to a deeper understanding of evolving social structures. Special attention is given to ethical considerations, risks of algorithmic bias, and the necessity of human-centered frameworks to ensure that AI-based models promote equitable and inclusive social development. The analysis is contextualized through international case studies and implications for developing countries, particularly in the Global South.

artificial intelligence; social modeling; social forecasting; complex systems; digital sociology; ethical AI; human-centered design; predictive analytics; philosophy of science; computational sociology.

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability and Transparency (pp. 149–159). https://doi.org/10.1145/3287560.3287583

Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.

Castells, M. (2010). The rise of the network society: The information age: Economy, society, and culture (Vol. 1, 2nd ed.). Wiley-Blackwell.

Floridi, L. (2019). The logic of information: A theory of philosophy as conceptual design. Oxford University Press.