Health Care Misinformation: an Artificial Intelligence Challenge for Low-resource languages.

In this paper, we motivate using state-of-the-art artificial intelligence technologies to address challenges presented by low-resource languages. We also reflect on both the importance and priorities of AI research with respect to the less wealthy economies of the world. We explore the contributions of colonialism to language (in)accessibility and public health misinformation during the Covid-19 pandemic in the African region. Using the West African country of Mali as a case study, we discuss the historic contribution of colonial educational systems to the creation of disenfranchised populations. These populations are left with limited access to important medical information that can mean life or death in the current Covid-19 pandemic. We propose a humans-in-the-loop neural machine translation, (NMT), solution to medical information translation. In our solution, the state-of-the-art NMT approach is applied to the low-resource language Bambara which is spoken by a majority of the Malian people. By implementing a crowdsourced Bambara language data collection and translation component in this machine learning problem, we engage the local Malians. The aim of this project is to address the lack of Bambara language resources and leverage current best practice in order to undo some of the artefacts of colonialism. We describe the unique challenges and research issues raised by this novel application of AI technology. Copyright © 2020 for this paper by its authors.