  Social and political scientists often aim to discover and measure distinct
biases from text data representations (embeddings). Innovative
transformer-based language models produce contextually-aware token embeddings
and have achieved state-of-the-art performance for a variety of natural
language tasks, but have been shown to encode unwanted biases for downstream
applications. In this paper, we evaluate the social biases encoded by
transformers trained with the masked language modeling objective using proposed
proxy functions within an iterative masking experiment to measure the quality
of transformer models' predictions, and assess the preference of MLMs towards
disadvantaged and advantaged groups. We compare bias estimations with those
produced by other evaluation methods using two benchmark datasets, finding
relatively high religious and disability biases across considered MLMs and low
gender bias in one dataset relative to the other. Our measures outperform
others in their agreement with human annotators. We extend on previous work by
evaluating social biases introduced after re-training an MLM under the masked
language modeling objective (w.r.t. the model's pre-trained base), and find
that proposed measures produce more accurate estimations of relative preference
for biased sentences between transformers than others based on our methods.
