  Masked Language Models (MLMs) have shown superior performances in numerous
downstream NLP tasks when used as text encoders. Unfortunately, MLMs also
demonstrate significantly worrying levels of social biases. We show that the
previously proposed evaluation metrics for quantifying the social biases in
MLMs are problematic due to following reasons: (1) prediction accuracy of the
masked tokens itself tend to be low in some MLMs, which raises questions
regarding the reliability of the evaluation metrics that use the (pseudo)
likelihood of the predicted tokens, and (2) the correlation between the
prediction accuracy of the mask and the performance in downstream NLP tasks is
not taken into consideration, and (3) high frequency words in the training data
are masked more often, introducing noise due to this selection bias in the test
cases. To overcome the above-mentioned disfluencies, we propose All Unmasked
Likelihood (AUL), a bias evaluation measure that predicts all tokens in a test
case given the MLM embedding of the unmasked input. We find that AUL accurately
detects different types of biases in MLMs. We also propose AUL with attention
weights (AULA) to evaluate tokens based on their importance in a sentence.
However, unlike AUL and AULA, previously proposed bias evaluation measures for
MLMs systematically overestimate the measured biases, and are heavily
influenced by the unmasked tokens in the context.
