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Machine Learning for the Developing World (ML4D): Challenges and Risks
Maria De-Arteaga · Amanda Coston · Tejumade Afonja

Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ West 121 + 122
Event URL: https://sites.google.com/view/ml4d/home »

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 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. 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, 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 and panel discussions. We welcome paper submissions featuring novel machine learning research that characterizes or tackle 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.

Author Information

Maria De-Arteaga (Carnegie Mellon University)
Amanda Coston (Carnegie Mellon University)
Tejumade Afonja (Saarland University)

Tejumade Afonja is a Graduate Student at Saarland University studying Computer Science. Previously, she worked as an AI Software Engineer at InstaDeep Nigeria. She holds a B.Tech in Mechanical Engineering from Ladoke Akintola University of Technology (2015). She’s currently a remote research intern at Vector Institute where she is conducting research in the areas of privacy, security, and machine learning. Tejumade is the co-founder of AI Saturdays Lagos, an AI community in Lagos, Nigeria focused on conducting research and teaching machine learning related subjects to Nigerian youths. Tejumade is one of the 2020 Google EMEA Women Techmakers Scholar. Tejumade was a co-organizer for ML4D 2019 NeurIPS workshop and she is serving as the lead organizer this year. She is affiliated with several other workshops like BIA, WIML, ICLR, Deep Learning Indaba, AI4D, and DSA where she occasionally serves as a volunteer or mentor.

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