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Workshop

Machine Learning for the Developing World (ML4D): Achieving sustainable impact

William Herlands · Maria De-Arteaga · Amanda Coston

Room 510 BD

Global development experts are beginning to employ ML for diverse problems such as aiding rescue workers allocate resources during natural disasters, providing intelligent educational and healthcare services in regions with few human experts, and detecting corruption in government contracts. While ML represents a tremendous hope for accelerated development and societal change, it is often difficult to ensure that machine learning projects provide their promised benefit. The challenging reality in developing regions is that pilot projects disappear after a few years or do not have the same effect when expanded beyond the initial test site, and prototypes of novel methodologies are often never deployed.

At the center of this year’s program is how to achieve sustainable impact of Machine Learning for the Developing World (ML4D). This one-day workshop will bring together a diverse set of participants from across the globe to discuss major roadblocks and paths to action. Practitioners and development experts will discuss essential elements for ensuring successful deployment and maintenance of technology in developing regions. Additionally, the workshop will feature cutting edge research in areas such as transfer learning, unsupervised learning, and active learning that can help ensure long-term ML system viability. Attendees will learn about contextual components to ensure effective projects, development challenges that can benefit from machine learning solutions, and how these problems can inspire novel machine learning research.

The workshop will include invited and contributed talks, a poster session of accepted papers, panel discussions, and breakout sessions tailored to the workshop theme. We welcome paper submissions focussing on core ML methodology addressing ML4D roadblocks, application papers that showcase successful examples of ML4D, and research that evaluates the societal impact of ML.

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Timezone: America/Los_Angeles

Schedule

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