NLP Bias Against Disabled People

An overview of how biases against mentions of disabilities are embedded in natural language processing tasks and models

I recently came across “Social Biases in NLP Models as Barriers for Persons with Disabilities”, a new paper (arXiv preprint here) that will appear in ACL 2020. It provided a novel vantage point on bias in NLP by looking at how machine learning and NLP models affect disabled people, which I found to be a very interesting perspective and one I sought to highlight.

Photo by Josh Appel on Unsplash

One Line Summary

Undesirable biases against disabled people exist in NLP tasks and models, specifically toxicity prediction, sentiment analysis, and word embeddings.

Motivation & Background

NLP models are increasingly being used in our daily lives in a variety of ways:

• To detect and moderate toxic comments in online forums (toxicity prediction)
• To measure consumers’ feelings (sentiment analysis) towards well-known brands
• To match candidates to job opportunities

With such prevalent usage, it is crucial for NLP models to not discriminate against people impacted by these algorithms. Previous research exploring…