Machine Learning for the Developing World

By Maria De-Arteaga, William Herlands, Daniel B. Neill, Artur Dubrawski. 2018

75 citations recorded by the source corpus.

Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges. This article examines the burgeoning field of machine learning for the developing world (ML4D). First, we present a review of prominent literature. Next, we suggest best practices drawn from the literature for ensuring that ML4D projects are relevant to the advancement of development objectives. Finally, we discuss how developing world challenges can motivate the design of novel machine learning methodologies. This article provides insights into systematic differences between ML4D and more traditional machine learning applications. It also discusses how technical complications of ML4D can be treated as novel research questions, how ML4D can motivate new research directions, and where machine learning can be most useful.

Subfields: Information Systems, Media Technology, Transportation, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Vittorio Ferrari
2. Ramesh Raskar
3. Yoshua Bengio
4. Dora Zhao
5. Olga Russakovsky
6. Alexandra Sasha Luccioni
7. Angelina Wang
8. Arvind Narayanan

In Media Technology

1. Vittorio Ferrari
2. Ramesh Raskar
3. Yoshua Bengio
4. Abhishek Singh
5. Olga Russakovsky
6. Alexandra Sasha Luccioni
7. Caroline Pantofaru
8. Konrad Paul Kording

In Transportation

1. Ramesh Raskar
2. Yoshua Bengio
3. Olga Russakovsky
4. Praneeth Vepakomma
5. Angelina Wang
6. Felix Creutzig

In AI Ethics, Fairness & Society

1. Angelina Wang
2. Dora Zhao
3. Olga Russakovsky
4. Alexandra Sasha Luccioni
5. Arvind Narayanan
6. Alexandre Lacoste
7. Yuta Nakashima
8. Yusuke Hirota

Citations in the corpus

• A Step Toward More Inclusive People Annotations for Fairness (2021, background): “, bridegroom) [25] and object classes (e.”
• SplitNN-driven Vertical Partitioning (2020, methodology): “Vertical federated learning Learning with vertically partitioned data in the context of federated learning, a popular distributed deep learning paradigm was studied in (Nock et al. , 2018; Liu et al. , 2019, 2020; Sameer et al. , 2018; Vepakomma et al. , 2018b; Sharma et al. , 2019a,b).”
• Tackling Climate Change with Machine Learning (2019, background): “rther encourage public entities to release data that may be relevant to climate change mitigation and adaptation goals. 2See the AI for social good movement (e.g. [5, 6]), ML for the developing world [7], the computational sustainability movement (e.g. [8–12], the American Meteorological Society’s C”
• “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, unknown): “, [31, 98, 107], understanding socio-cultural factors e.”
• REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets (2020, background): “Researchers have looked at OpenImages and ImageNet and found these datasets to be Amerocentric and Eurocentric (Shankar et al., 2017), with models dropping in performance when being run on images from underrepresented locales.”