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The awareness and mitigation of biases are of fundamental importance for thefair and transparent use of contextual language models, yet they cruciallydepend on the accurate detection of biases as a precursor. Consequently,numerous bias detection methods have been proposed, which vary in theirapproach, the considered type of bias, and the data used for evaluation.However, while most detection methods are derived from the word embeddingassociation test for static word embeddings, the reported results areheterogeneous, inconsistent, and ultimately inconclusive. To address thisissue, we conduct a rigorous analysis and comparison of bias detection methodsfor contextual language models. Our results show that minor design andimplementation decisions (or errors) have a substantial and often significantimpact on the derived bias scores. Overall, we find the state of the field tobe both worse than previously acknowledged due to systematic and propagatederrors in implementations, yet better than anticipated since divergent resultsin the literature homogenize after accounting for implementation errors. Basedon our findings, we conclude with a discussion of paths towards more robust andconsistent bias detection methods.

Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models