The rapid advancement of artificial intelligence (AI) technologies has significantly transformed the way social scientists and policymakers understand, model, and anticipate societal change. AI is not only a computational tool but also a catalyst for reimagining the dynamics of social systems, enabling the prediction of emergent behaviors, identification of hidden patterns, and simulation of complex interactions across different levels of society. This paper examines the epistemological and methodological implications of using AI in the design and forecasting of social dynamics. Drawing on interdisciplinary approaches from philosophy of science, systems theory, and digital sociology, the study explores 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, the risks of algorithmic bias, and the necessity of human-centered frameworks in ensuring that AI-driven models support 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

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 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.

Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51–59. https://doi.org/10.1038/nature12047

Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., ... & Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742