Call for papers: 3rd NeurIPS workshop on ML for the Developing World (ML4D)

[Maria De Arteaga Gonzalez]

Hi all,

We are very excited to announce the 3rd NeurIPS workshop on Machine
Learning for the Developing World (ML4D), please see the CFP below. We'd
also appreciate your help spreading the word. Thanks!

Maria

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

*********************************************************************************

Workshop on Machine Learning for the Developing World, NeurIPS 2019

Date: December 13th or 14th (TBD), 2019

Location: Vancouver, Canada

Website: https://sites.google.com/view/ml4d/home

*********************************************************************************

Call for papers:

For the third year in a row, NeurIPS is host to a one-day workshop focussed
on machine learning for the developing world (ML4D). This year’s program
will focus on the challenges and risks that arise when deploying machine
learning in developing regions.


We invite researchers to submit their recent work on ML4D, and particularly
encourage submissions that characterize challenges or risks of ML4D,
propose methodology tackling existing limitations, present empirical
studies that reveal unintended harms of machine learning technology in
developing regions, or discuss the current state of the art and propose
paths forward.

Please submit 2-4 page extended abstracts following the NeurIPS style
<https://nips.cc/Conferences/2017/PaperInformation/StyleFiles> guidelines.
The link to the submission platform will be provided in the workshop
website. 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: September 13, 2019

Travel/registration award deadline: September 13, 2019

Acceptance notification: September 25, 2019

Workshop: December 13/14 (TBD), 2019


Workshop overview:


As the use of machine learning becomes ubiquitous, there is growing
interest in understanding how machine learning can be used to tackle global
development challenges. The possibilities are vast, and it is important
that we explore the potential benefits of such technologies, which has
driven the agenda of the ML4D workshop series in the past. However, there
is a risk that technology optimism and a categorization of ML4D research as
inherently “social good” may result in initiatives failing to account for
unintended harms or deviating scarce funds towards initiatives that appear
exciting but have no demonstrated effect. Moreover, machine learning
technologies deployed in developing regions have often been created for
different contexts and are trained with data that is not representative of
the new deployment setting. Most concerning of all, multinational companies
sometimes make the deliberate choice to deploy new technologies in
countries with little regulation in order to experiment. This year’s
program will focus on the challenges and risks that arise when deploying
machine learning in developing regions. This one-day workshop will bring
together a diverse set of participants from across the globe to discuss
essential elements for ensuring ML4D research moves forward in a
responsible and ethical manner. Attendees will learn about potential
unintended harms that may result from ML4D solutions, technical challenges
that currently prevent the effective use of machine learning in vast
regions of the world, and lessons that may be learned from other fields.
The workshop will include invited talks, a poster session of accepted
papers, breakout sessions tailored to the workshop's theme and panel
discussions. We welcome paper submissions featuring novel machine learning
research that characterizes or tackles challenges of ML4D, empirical papers
that reveal unintended harms of machine learning technology in developing
regions, and discussion papers that examine the current state of the art of
ML4D and propose paths forward.


Maria De-Arteaga
PhD Student in Machine Learning and Public Policy
Carnegie Mellon University
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20190727/ac0ebe6f/attachment.html>