ABSTRACT
This chapter surveys how AI expands what social scientists can observe, analyze, and explain. It details the synergy between pervasive digital traces and algorithmic methods, highlighting how classifiers, embeddings, simulations, and generative models unlock insights at the population scale while inviting new questions about validity and interpretability. It balances opportunity (enhanced scale, new constructs, and rapid iteration) with risk (bias, opacity, drift, and unequal access to data/compute). Methodologically, it argues for complementarity – using AI to propose patterns and humans to supply meaning, mechanisms, and normative evaluation. Epistemologically, it emphasizes that prediction must be tethered to theory and that explainability practices are essential for trust. Finally, it outlines the volume’s chapter arc from core techniques to disciplinary applications and integrative ethics.
