Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis
This work addresses implicit biases in sentiment analysis models, which is important for fairness in downstream applications, though it is incremental as it builds on existing bias detection methods.
The authors tackled the problem of occupational gender stereotypes in sentiment analysis models by creating a gender-balanced dataset of 800 sentences and evaluating three models, finding that these models exhibit biases that correlate with societal perceptions.
In this work, we investigate the presence of occupational gender stereotypes in sentiment analysis models. Such a task has implications for reducing implicit biases in these models, which are being applied to an increasingly wide variety of downstream tasks. We release a new gender-balanced dataset of 800 sentences pertaining to specific professions and propose a methodology for using it as a test bench to evaluate sentiment analysis models. We evaluate the presence of occupational gender stereotypes in 3 different models using our approach, and explore their relationship with societal perceptions of occupations.