This book explains how artificial intelligence is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. The volume offers a clear route into machine learning, natural language processing, simulation, and generative AI for social researchers.
AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.
Key features include:
· Practical guidance on integrating AI into social science research workflows without losing theoretical and contextual judgement
· Coverage of core techniques, including ML for prediction and pattern discovery and NLP for large-scale textual and qualitative data
· Discussion of explainability and “black-box” risks, with approaches for interpretability and responsible use
· Cross-disciplinary examples spanning sociology, psychology, anthropology, political science, economics, and management
· A critical account of research integrity issues: data quality, bias, privacy, accountability, and the limits of automation
Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.
