Assessing gender bias in medical and scientific masked language models with StereoSet

TL;DR

This paper evaluates gender and social biases in medical and general language models using StereoSet, revealing medical models generally exhibit more bias, especially in gender and religion categories, due to differences in training data.

Contribution

It provides a comparative analysis of biases in medical versus general-purpose masked language models using StereoSet, highlighting the influence of training data on bias levels.

Findings

General-purpose MLMs show significant bias, especially in gender.

Medical MLMs generally exhibit more bias than general models.

Training data differences likely cause variations in bias levels.

Abstract

NLP systems use language models such as Masked Language Models (MLMs) that are pre-trained on large quantities of text such as Wikipedia create representations of language. BERT is a powerful and flexible general-purpose MLM system developed using unlabeled text. Pre-training on large quantities of text also has the potential to transparently embed the cultural and social biases found in the source text into the MLM system. This study aims to compare biases in general purpose and medical MLMs with the StereoSet bias assessment tool. The general purpose MLMs showed significant bias overall, with BERT scoring 57 and RoBERTa scoring 61. The category of gender bias is where the best performances were found, with 63 for BERT and 73 for RoBERTa. Performances for profession, race, and religion were similar to the overall bias scores for the general-purpose MLMs.Medical MLMs showed more bias in all categories than the general-purpose MLMs except for SciBERT, which showed a race bias score of 55, which was superior to the race bias score of 53 for BERT. More gender (Medical 54-58 vs. General 63-73) and religious (46-54 vs. 58) biases were found with medical MLMs. This evaluation of four medical MLMs for stereotyped assessments about race, gender, religion, and profession showed inferior performance to general-purpose MLMs. These medically focused MLMs differ considerably in training source data, which is likely the root cause of the differences in ratings for stereotyped biases from the StereoSet tool.