Social Biases in NLP Models as Barriers for Persons with Disabilities
Table 3: Terms that are over-represented in comments with mentions of the psychiatric_or_mental_illness based on the Jigsaw (2019) dataset, grouped across the five categories described in Section 5. Score represents the log-odds ratio as calculated using Monroe et al. (2008); a score greater than 1.96 is considered statistically significant.
['condition mentally ill', 'Score 23.1', 'treatment help', 'Score 9.7', 'infra. hospital', 'Score 6.3', 'linguistic people', 'Score 9.0', 'social homeless', 'Score 12.2']
As expected, the condition phrases have the highest association. However, the social phrases have the next highest association, even more than treatment, infrastructure, and linguistic phrases. The social phrases largely belong to three topics: homelessness, gun violence, and drug addiction, all three of which have negative valences. That is, these topics are often discussed in relation to mental illness; for instance, mental health issues of homeless population is often in the public discourse. While these associations are perhaps not surprising, it is important to note that these associations with topics of arguably negative valence significantly shape the way disability terms are represented within NLP models, and that in-turn may be contributing to the model biases we observed in the previous sections.