Adapting Machine Learning Techniques for Low-Resource Settings in Developing Countries: A Multidisciplinary Approach

Developing countries face unique challenges in harnessing the power of machine learning (ML) due to limited resources, data scarcity, and imbalanced representations. This paper aims to address these challenges by exploring advances in algorithms and methods tailored for low-resource settings, as well as examining industry practices and societal impacts of ML solutions in developing countries. We present a multidisciplinary approach, combining the fields of computer science, social sciences, and policy-making to ensure the development and deployment of inclusive and sustainable ML solutions.