Abstract
This paper explores the integration of machine learning into economics and social sciences, assessing its potential impact and limitations. It introduces fundamental machine learning concepts and principles, highlighting the differences between the two disciplines, particularly the focus on causal inference in economics and prediction in machine learning. The paper discusses diverse applications of machine learning, from extracting insights from unstructured data to creating novel indicators and improving predictive accuracy, while also addressing challenges related to data quality, computational efficiency, and data ownership. It emphasizes the importance of standardization, transparency, and ethical considerations in prediction tasks, recognizing that machine learning is a powerful tool but cannot replace economic theory. Ultimately, researchers remain optimistic about the transformative potential of machine learning in re-shaping research methodologies and generating new insights in economics and social sciences.