Machine learning for the developing world

Maria De-Arteaga, William Herlands, Daniel B. Neill, Artur Dubrawski

Research output: Contribution to journalArticlepeer-review


Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges. This article examines the burgeoning field of machine learning for the developing world (ML4D). First, we present a review of prominent literature. Next, we suggest best practices drawn from the literature for ensuring that ML4D projects are relevant to the advancement of development objectives. Finally, we discuss how developing world challenges can motivate the design of novel machine learning methodologies. This article provides insights into systematic differences between ML4D and more traditional machine learning applications. It also discusses how technical complications of ML4D can be treated as novel research questions, how ML4D can motivate new research directions, and where machine learning can be most useful.

Original languageEnglish (US)
Article number9
JournalACM Transactions on Management Information Systems
Issue number2
StatePublished - Apr 2018


  • Developing countries
  • Global development

ASJC Scopus subject areas

  • Management Information Systems
  • General Computer Science


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