AI in Social Sciences Research | 2.9 MB
Title: AI in Social Sciences Research
Author: Łukasz Sułkowski, Marcin Lis, Sabina Ratajczak, Zdzisława Dacko-Pikiewicz
Category: Nonfiction, Social & Cultural Studies, Social Science, Methodology, Reference & Language, Reference, Research
Language: English | 165 Pages | ISBN: 3319168282
Description:
AI in Social Sciences Research explains how artificial intelligence is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a toolkit and a methodological shift, the volume offers a clear route into machine learning, natural language processing, simulation, and generative AI 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 machine learning 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 methods, the book supports readers who want to use AI with methodological care, not just technical speed.
AI in Social Sciences Research explains how artificial intelligence is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a toolkit and a methodological shift, the volume offers a clear route into machine learning, natural language processing, simulation, and generative AI 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 machine learning 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 methods, the book supports readers who want to use AI with methodological care, not just technical speed.
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