CFP: NIPS Workshop on Machine Learning for the Developing World

[Maria De Arteaga]

Call for Papers - NIPS 2017 Workshop on Machine Learning for the Developing World


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Workshop on Machine Learning for the Developing World, NIPS 2017
Date: December 8th, 2017
Location: Long Beach, California, USA
https://sites.google.com/site/ml4development/
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Call for papers:

This one-day workshop is focussed on machine learning for the developing world (ML4D). We will discuss impactful applications of machine learning to address core global development concerns, as well as limitations to ML in developing countries and novel algorithms inspired by development challenges, such as limited computational capacity.

We invite researchers to submit their recent work on this topic, including:
* Applications of ML to development issues including health, education, institutional integrity, violence mitigation, economics, societal analysis, and environment.
* Novel ML techniques inspired by limitations in developing countries.
* Limitations and risks of data science and ML for development.
* Practical systems using ML in developing regions.

Please submit 2-4 page extended abstracts to ml4d...@gmail.com, following the NIPS style guidelines. Accepted papers will be presented as posters or contributed talks, and may optionally be published in an arXiv proceedings.


Key dates:
Submission deadline: October 20, 2017
Acceptance notification: November 1, 2017
Workshop: December 8, 2017


Speakers:
-- Emma Brunskill (Stanford)
-- Stefano Ermon (Stanford)
-- Daniel Neill (CMU)
-- Patrick Ball (Human Rights Data Analysis Group)
-- Jen Ziemke (International Network of Crisis Mappers)
-- John Quinn (UN Global Pulse)

Workshop overview:
Six billion people live in developing world countries. The unique development challenges faced by these regions have long been studied by researchers ranging from sociology to statistics and ecology to economics. With the emergence of mature machine learning methods in the past decades, researchers from many fields - including core machine learning - are increasingly turning to machine learning to study and address challenges in the developing world. This workshop is about delving into the intersection of machine learning and development research.

Machine learning present tremendous potential to development research and practice. Supervised methods can provide expert telemedicine decision support in regions with few resources; deep learning techniques can analyze satellite imagery to create novel economic indicators; NLP algorithms can preserve and translate obscure languages, some of which are only spoken. Yet, there are notable challenges with machine learning in the developing world. Data cleanliness, computational capacity, power availability, and internet accessibility are more limited than in developed countries. Additionally, the specific applications differ from what many machine learning researchers normally encounter. The confluence of machine learning's immense potential with the practical challenges posed by developing world settings has inspired a growing body of research at the intersection of machine learning and the developing world.

This one-day workshop is focussed on machine learning for the developing world, with an emphasis on developing novel methods and technical applications that address core concerns of developing regions. We will consider a wide range of development areas including health, education, institutional integrity, violence mitigation, economics, societal analysis, and environment. From the machine learning perspective we are open to all methodologies with an emphasis on novel techniques inspired by particular use cases in the developing world.

Invited speakers will address particular areas of interest, while poster sessions and a guided panel discussion will encourage interaction between attendees. We wish to review the current approaches to machine learning in the developing world, and inspire new approaches and paradigms that can lay the groundwork for substantial innovation.