Machine Learning and Data Science in Social Sciences: Methods, Applications, and Future Directions

Artificial intelligence (AI) is transforming social science research by enabling scalable data analysis, predictive modeling, and causal inference, thereby reshaping the methodological foundations of fields such as political science, economics, and psychology. This survey examines the application of key AI techniques—particularly machine learning (ML), natural language processing (NLP), network science, and explainable AI (XAI)—in addressing domain-specific challenges in empirical research. Emphasis is placed on methodological integration, including bias mitigation, fairness-aware learning, causal discovery, and model interpretability. While these tools enhance analytical capacity, they raise critical concerns about algorithmic bias, transparency, and ethical accountability. We review current strategies for responsible AI deployment, including regulatory frameworks, human-centered design principles, and privacy-preserving methods. By synthesizing methodological advances and cross-cutting challenges, this study provides a focused and interdisciplinary roadmap for the rigorous and equitable use of AI in social science research.