  Most well-established data collection methods currently adopted in NLP depend
on the assumption of speaker literacy. Consequently, the collected corpora
largely fail to represent swathes of the global population, which tend to be
some of the most vulnerable and marginalised people in society, and often live
in rural developing areas. Such underrepresented groups are thus not only
ignored when making modeling and system design decisions, but also prevented
from benefiting from development outcomes achieved through data-driven NLP.
This paper aims to address the under-representation of illiterate communities
in NLP corpora: we identify potential biases and ethical issues that might
arise when collecting data from rural communities with high illiteracy rates in
Low-Income Countries, and propose a set of practical mitigation strategies to
help future work.
