Machine Learning for Social Good: Applications in Non-Profit and Public Sectors

Abstract

Machine Learning (ML) has shown immense potential in driving change and improving outcomes in various sectors, particularly in non-profit organizations and the public sector. This paper explores the various ways in which ML is being applied to address social issues such as poverty, healthcare, education, and sustainability. It highlights both the challenges and successes in using data-driven solutions for social good. By examining case studies and models, this paper provides insights into how ML can empower organizations to make informed decisions, enhance operational efficiency, and maximize social impact. The research also identifies future opportunities and the ethical considerations inherent in applying machine learning in the non-profit and public sectors.