Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
Year: 2017
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Blodgett, Su Lin, O'Connor, Brendan
Institution: University of Massachusetts Amherst
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W2728567418