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Data Science for Social Impact

Our general areas of work straddle Data Science for Society as well as Local Language Natural Language Processing. These two strands are complementary. Our work in Data Science and Society has allowed us to have a more nuanced approach to understanding the systematic challenges that face being able to do excellent science with local languages. Through Data Science for Society, we have to understand how when one carries through Data Science research, we situate how the users are part of the process. We find that we need to adjust our research to take care of these challenges and innovate in ways we gather direct data or alternative data.

For us, Data Science for Society means being able to improve approaches/methods or scientific tools for DS while enhancing the ways decision-makers can use the insights that come from these tools. Local Language Natural Language Processing is focused on ways to develop new tools, new data and methodology to improve the state of African languages.

Research Fields

  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Social Media (SM)
  • Society (Soc)
  • Web Technologies (WT)

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