Call for papers: NIPS workshop on ML for the Developing World (ML4D)

[Maria De Arteaga Gonzalez]

Dear Autonians,

For the second year in a row, Will Herlands and I are organizing a NIPS
workshop on *Machine Learning for the Developing World *that may be of
interest to some of you. We'd also appreciate your help spreading the word.
Thanks!

Maria


Call for papers: NIPS workshop on ML for the Developing World (ML4D)

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Workshop on Machine Learning for the Developing World, NIPS 2018

Date: December 8th, 2018

Location: Montreal, Canada

Website: sites.google.com/view/ml4d-nips-2018
<http://www.sites.google.com/view/ml4d-nips-2018>

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Now available:

   -

   Travel awards! (apply here
   <https://docs.google.com/forms/d/e/1FAIpQLSd5aY8SwS57UArWto3eU6-m6mF7JfSX4f_cgWaMZJJKKxCN6A/viewform>
   )
   -

   Workshop registration awards! (apply here
   <https://docs.google.com/forms/d/e/1FAIpQLScrSfv2kIbXdOaUbnC4008lboHGmZnSgqXY9mL72ZA1lUMbJA/viewform>
   )
   -

   Deadline: October 25


Call for papers:

For the second year in a row, NIPS is host to a one-day workshop focused on
machine learning for the developing world (ML4D). This year’s workshop
focuses on creating sustainable impact within ML4D that can advance both
machine learning research and global development objectives.

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 development challenges.

* Technical or procedural systems to ensure sustainable ML4D projects.

* Limitations and risks of data science and ML for development.

Please submit 2-4 page extended abstracts to ml4d.nips at gmail.com, following
the NIPS style
<https://nips.cc/Conferences/2017/PaperInformation/StyleFiles> guidelines.
Accepted papers will be presented as posters or contributed talks, and may
opt-in to be published in an arXiv proceedings.


Key dates:

Submission deadline: October 25, 2018

Travel/registration award deadline: October 25, 2018

Acceptance notification: November 5, 2018

Workshop: December 8, 2018

Speakers:

-- Daniel Neill (NYU)

-- Monica Meltis (Data Civica)

-- Anandan (Wadhwaniai Institute)

-- Sriganesh Lokanathan (LIRNE Asia)

-- Nyalleng Moorosi (Council for Scientific and Industrial Research, South
Africa)

Workshop overview:

Global development experts are beginning to employ ML for diverse problems
such as aiding rescue workers allocate resources during natural disasters,
providing intelligent educational and healthcare services in regions with
few human experts, and detecting corruption in government contracts. While
ML represents a tremendous hope for accelerated development and societal
change, it is often difficult to ensure that machine learning projects
provide their promised benefit. The challenging reality in developing
regions is that pilot projects disappear after a few years or do not have
the same effect when expanded beyond the initial test site, and prototypes
of novel methodologies are often never deployed.

At the center of this year’s program is how to achieve sustainable impact
of Machine Learning for the Developing World (ML4D). This one-day workshop
will bring together a diverse set of participants from across the globe to
discuss major roadblocks and paths to action. Practitioners and development
experts will discuss essential elements for ensuring successful deployment
and maintenance of technology in developing regions. Additionally, the
workshop will feature cutting edge research in areas such as transfer
learning, unsupervised learning, and active learning that can help ensure
long-term ML system viability. Attendees will learn about contextual
components to ensure effective projects, development challenges that can
benefit from machine learning solutions, and how these problems can inspire
novel machine learning research.

The workshop will include invited and contributed talks, a poster session
of accepted papers, panel discussions, and breakout sessions tailored to
the workshop theme. We welcome paper submissions focussing on core ML
methodology addressing ML4D roadblocks, application papers that showcase
successful examples of ML4D, and research that evaluates the societal
impact of ML.
María De Arteaga
PhD Student in Machine Learning and Public Policy
Carnegie Mellon University
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