Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
published: Dec. 1, 2017, recorded: August 2017, views: 1300
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !