  Currently, natural language processing (NLP) models proliferate language
discrimination leading to potentially harmful societal impacts as a result of
biased outcomes. For example, part-of-speech taggers trained on Mainstream
American English (MAE) produce non-interpretable results when applied to
African American English (AAE) as a result of language features not seen during
training. In this work, we incorporate a human-in-the-loop paradigm to gain a
better understanding of AAE speakers' behavior and their language use, and
highlight the need for dialectal language inclusivity so that native AAE
speakers can extensively interact with NLP systems while reducing feelings of
disenfranchisement.
