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Abstract:

We will present a data-centric framework for making machine learning more trustworthy in developing countries. We will discuss best practices for data in different stages of the ML pipeline: starting with how to design/curate datasets, followed by how to identify informative data for ML, and then how to audit and debug ML models to ensure reliable application in resource-limited scenarios.

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