TY - JOUR
T1 - Machine learning for the developing world
AU - De-Arteaga, Maria
AU - Herlands, William
AU - Neill, Daniel B.
AU - Dubrawski, Artur
N1 - Funding Information:
This material is based upon work supported by NSF Graduate Research Fellowship DGE-1252522, NSF awards IIS-0953330 and IIS-1563887, and DARPA awards FA8750-12-2-0324 and 750-14-2-0244. Authors’ addresses: M. De-Arteaga (Corresponding author), Machine Learning Department, Heinz College, Auton Lab, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213; email: [email protected]; W. Herlands, Machine Learning Department, Heinz College, Event and Pattern Detection Laboratory, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213; email: [email protected]; D. B. Neill, Machine Learning Department, Heinz College, Event and Pattern Detection Laboratory, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213; email: [email protected]; A. Dubrawski, Auton Lab, Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2018 ACM 2158-656X/2018/08-ART9 $15.00 https://doi.org/10.1145/3210548
Publisher Copyright:
© 2018 ACM.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
KW - Developing countries
KW - Global development
UR - http://www.scopus.com/inward/record.url?scp=85053510922&partnerID=8YFLogxK
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U2 - 10.1145/3210548
DO - 10.1145/3210548
M3 - Article
AN - SCOPUS:85053510922
SN - 2158-656X
VL - 9
JO - ACM Transactions on Management Information Systems
JF - ACM Transactions on Management Information Systems
IS - 2
M1 - 9
ER -