  We highlight an important frontier in algorithmic fairness: disparity in the
quality of natural language processing algorithms when applied to language from
authors of different social groups. For example, current systems sometimes
analyze the language of females and minorities more poorly than they do of
whites and males. We conduct an empirical analysis of racial disparity in
language identification for tweets written in African-American English, and
discuss implications of disparity in NLP.
