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Most well-established data collection methods currently adopted in NLP dependon the assumption of speaker literacy. Consequently, the collected corporalargely fail to represent swathes of the global population, which tend to besome of the most vulnerable and marginalised people in society, and often livein rural developing areas. Such underrepresented groups are thus not onlyignored when making modeling and system design decisions, but also preventedfrom benefiting from development outcomes achieved through data-driven NLP.This paper aims to address the under-representation of illiterate communitiesin NLP corpora: we identify potential biases and ethical issues that mightarise when collecting data from rural communities with high illiteracy rates inLow-Income Countries, and propose a set of practical mitigation strategies tohelp future work.

Building Representative Corpora from Illiterate Communities: A Review of Challenges and Mitigation Strategies for Developing Countries