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

Fri Dec 13th 08:00 AM -- 06:00 PM @ West 121 + 122
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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.

08:30 AM Opening Remarks (Talk)
08:45 AM AI's Blindspots and Where to Find Them (Invited Talk) Deborah Raji
09:15 AM Algorithmic Colonization (Invited Talk) Abeba Birhane
09:45 AM Coffee Break (Break)
10:30 AM Lessons from ICTD -- Information & Communication Tech (Invited Talk) Kentaro Toyama
11:00 AM Poster session <span> <a href="#"></a> </span>
Michael Melese Woldeyohannis, Bernardt Duvenhage, Nyamos Waigama, Asaye Bir Senay, Claire Babirye, Tensaye yibeltal Ayalew, Kelechi Ogueji, Vinay Prabhu, Prabu Ravindran, Olamilekan Wahab, chukwunonso H Nwokoye, Paul Duckworth, Hafte Abera, Abebe Mideksa, Loubna Benabbou, Anugraha Sinha, Ivan Kiskin, Robert Soden, Tupokigwe Isagah, Rehema Mwawado, Yimer Hussien, Bryan Wilder, Daniel Omeiza, Sunayana Rane, Richard Mgaya, Samsun Knight, Jessenia Gonzalez Villarreal, Eyob Beyene, Monika Obrocka Tulinska, Luis Fernando Cantu Diaz de Leon, Joseph Aro, Michael T Smith, Michael Famoroti, Praneeth Vepakomma, Ramesh Raskar, Debjani Bhowmick, Chukwunonso H Nwokoye, Alejandro Noriega Campero, Hope Mbelwa, Anusua Trivedi
12:00 PM Lunch (Break)
02:00 PM Data sharing in and for Africa (Contributed talk) Sekou Remy
02:15 PM Risks of Using Non-verified Open Data: A case study on using Machine Learning techniques for predicting Pregnancy Outcomes in India (Contributed Talk) Anusua Trivedi
02:30 PM A Noxious Market for Personal Data (Contributed Talk) Abdul Abdulrahim
02:45 PM HumBug Zooniverse: a crowdsourced acoustic mosquito dataset (Contributed Talk) Ivan Kiskin
03:00 PM Mathematics of identity at trial: Digital ID at the constitutional court in Kenya (Invited talk) Bomu Mutung'u
03:30 PM Coffee and Posters (Break)
04:15 PM Rockefeller Foundation and ML4D (Talk) Eva Gjekmarkaj
04:20 PM Partnership on AI and ML4D (Talk) Alice Xiang
04:25 PM Wadhwani AI and ML4D (Talk) Amrita Mahale
04:30 PM Panel Discussion: Risks and Challenges in ML4D (Discussion Panel)
05:30 PM Closing Remarks and Town Hall (Discussion)

Author Information

Maria De-Arteaga (Carnegie Mellon University)

Maria is a joint PhD candidate in Machine Learning and Public Policy at Carnegie Mellon University’s Machine Learning Department and the Heinz College of Information Systems and Public Policy. Machine learning (ML) is increasingly being used to support decision-making in critical settings, where predictions have potentially grave implications over human lives. Examples include healthcare, hiring, child welfare, and criminal justice. Maria's research focuses on the risks and opportunities of ML-based predictions to support decision-making in the context of sustainable societies. As part of her work on algorithmic fairness and accountability, she characterizes how societal biases encoded in historical data may be reproduced and amplified by ML models, and develops algorithms to mitigate these risks. Moreover, even if data does not encode harmful societal biases, many challenges still prevent the effective use of predictions to improve decision-making, such as omitted payoff bias and the selective labels problem. In her research, Maria seeks to understand the limits and risks of using machine learning in these contexts, and to develop human-centered ML that can improve expert decision-making. She holds a M.Sc. in Machine Learning from Carnegie Mellon University (2017) and a B.Sc. in Mathematics from Universidad Nacional de Colombia (2013). She was an intern at Microsoft Research, Redmond, in 2017 and at Microsoft Research, New England, in 2018. Prior to graduate school, she worked as a data science researcher and as an investigative journalist. Her work has been awarded the Best Thematic Paper Award at NAACL’19, the Innovation Award on Data Science at Data for Policy’16, and has been featured by UN Women and Global Pulse in their report Gender Equality and Big Data: Making Gender Data Visible. She is a co-founder of the NeurIPS Machine Learning for the Developing World (ML4D) Workshop, and a recipient of a 2018 Microsoft Research Dissertation Grant.

Amanda Coston (Carnegie Mellon University)
Tejumade Afonja (Saarland University)

Tejumade is a Masters's student in Computer Science at Saarland University. She has previously 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 is an Intel Software Innovator for Machine Learning in Nigeria and the co-founder of AI Saturdays Lagos, an Artificial Intelligence community with the goal to democratize Artificial Intelligence by creating a community to help enable studying, researching and building AI products for their ecosystem and beyond. She is the co-author of ChowNet, an African food data collection project (ongoing) and she remotely presented this work at the 2nd Blackinai workshop at NeurIPS (2018). Tejumade's work with AI Saturdays has been featured on the Intel Innovator spotlight, Fast Company, TechCabal & AI4Dev.

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