The interdisciplinary research area From Prediction to Agile Interventions in the Social Sciences (FAIR) brings together researchers from the data sciences, statistics, education sciences, psychology, rehabilitation studies, and sociology. FAIR-researchers from these different disciplines will focus on the development and application of innovative research methods from the data sciences and use them to address societal challenges in highly relevant areas such as education, health, and societal inclusiveness and participation. Increasingly larger and more complex data have become available in the social sciences that can contribute to more precise prediction models (e.g., for such outcomes as academic success, health, and well-being) and aid our understanding of cause-effect relationships. An important objective is also the development of a framework for “Agile Intervention Research” that allows for individualized, data-driven, and need-based adaptations of interventions in authentic contexts. The FAIR-researchers will use “big” (large data sets) and “small” (small case numbers) data to optimize prognostic models in the social sciences and maximize the impact of available interventions in these fields by allowing for individualized adaptations.