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Machine learning for the developing world
De-Arteaga M., Herlands W., Neill D., Dubrawski A. ACM Transactions on Management Information Systems9 (2):1-14,2018.Type:Article
Date Reviewed: May 11 2020

The authors describe the ways in which machine learning for the developing world (referred to as ML4D) requires a different viewpoint from the kind of machine learning that concerns itself with the developed world. They assert that the aim of ML4D should be to produce applications and results that apply to specific problems of the developing world, using the data and resources available in the developing world. Specific problems include healthcare applications, understanding and modeling violence, the generation of economic outlooks, understanding population dynamics, and environmental studies for the mitigation of natural disasters.

It is not that these problems are peculiar to the developing world; rather, the resources and data available are fewer and smaller. This means ML4D research needs to take into account the local context. To this end, data reliability must be improved, directly applicable solutions should be supplied, and policymakers kept informed.

Perhaps the most interesting conclusion of the paper is that ML4D provides opportunities for advancing machine learning as a whole. Thus, deployed systems will require research into learning from small datasets and into learning from multiple messy datasets. Also, to make best use of limited resources, data acquisition should be intelligent. Learning components need to be able to function with limited memory, and intelligent compression algorithms are needed to account for limited telecommunication networks.

A substantial bibliography makes the paper a valuable and persuasive introduction to the topic of ML4D.

Reviewer:  J. P. E. Hodgson Review #: CR146964 (2010-0245)
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