Machine Learning and Data-Driven Decision-Making

Abstract

This paper examines machine learning and data-driven decision-making from a contemporary
academic perspective. The discussion considers major concepts, current developments,
practical applications, social or organizational implications, challenges and future directions.
The topic is relevant to researchers, educators, practitioners, policymakers and students
because changing social, economic and technological conditions are reshaping established
practices. The paper connects theoretical perspectives with practical considerations and
emphasizes evidence, context, responsible practice and continued research. It also considers
how changing expectations and emerging developments influence the subject.