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For the fifth year in a row, NeurIPS is host to a one-day workshop focused on Machine Learning for the Developing World (ML4D). This year’s program will focus on the role of machine learning for addressing global challenges in low income regions.
We invite researchers to submit their recent work on ML4D and particularly encourage submissions that engage with different ways machine learning can help tackle global challenges like the Covid pandemic or climate change and the way these manifest in the developing world. This year, we will accept submissions in two tracks:
Papers: We welcome submissions that approach ML4D from a variety of angles: These include papers that propose new methods motivated by development challenges in low-income regions, papers that describe the application or use of existing methods, and discussion papers that examine and critique the current state of ML4D and propose a way forward. Following our traditional approach, the Paper Track will accept short workshop contributions of 3-5 pages (including all text, tables and figures; excluding references).
Problem Pitches: We also welcome submissions of 1-2 page problem pitches outlining the background, scope, and feasibility of a newly proposed research project along with the underlying research problem. The problem pitches track allows for direct feedback on the new and proposed research, with the goal of better-integrating researchers from low-income countries and research on development issues into the machine learning community. For that purpose, the accepted submissions will be paired with a dedicated project mentor. On the day of the workshop, mentors and attending community members will be able to give feedback on the problem pitches in topic-specific breakout sessions.
Paper and proposal submission should follow the NeurIPS style guidelines (which include a $\LaTeX$ template). Accepted papers and problem pitches will be presented as posters or contributed talks and may opt-in to be published in arXiv proceedings. Submissions should be made via CMT.
All times are Anywhere-on-Earth (AoE).
While some nations are regaining normality after almost a year and a half since the COVID-19 pandemic struck–schools are reopening, face mask mandates are being dropped, economies are recovering–other nations, especially low-income countries, are still experiencing severe crises of their public health systems and economies. While the pandemic has been a global challenge, its effect on developing regions has been distinct. This situation makes us question how global challenges such as access to vaccines, good internet connectivity, sanitation, water, as well as poverty, climate change, environmental degradation, amongst others, have had and will have local consequences in developing nations, and how machine learning approaches can assist in designing solutions that take into account these local characteristics.
Past iterations of the ML4D workshop have explored: the development of smart solutions for intractable problems, the challenges and risks that arise when deploying machine learning models in developing regions, and building machine learning models with improved resilience. This year, we call on our community to identify and understand the particular challenges and consequences that global issues may result in developing regions while proposing machine learning-based solutions for tackling them.