Mind Your Bias: A Critical Review of Bias Detection Methods for
Contextual Language Models
- URL: http://arxiv.org/abs/2211.08461v1
- Date: Tue, 15 Nov 2022 19:27:54 GMT
- Title: Mind Your Bias: A Critical Review of Bias Detection Methods for
Contextual Language Models
- Authors: Silke Husse and Andreas Spitz
- Abstract summary: We conduct a rigorous analysis and comparison of bias detection methods for contextual language models.
Our results show that minor design and implementation decisions (or errors) have a substantial and often significant impact on the derived bias scores.
- Score: 2.170169149901781
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The awareness and mitigation of biases are of fundamental importance for the
fair and transparent use of contextual language models, yet they crucially
depend on the accurate detection of biases as a precursor. Consequently,
numerous bias detection methods have been proposed, which vary in their
approach, the considered type of bias, and the data used for evaluation.
However, while most detection methods are derived from the word embedding
association test for static word embeddings, the reported results are
heterogeneous, inconsistent, and ultimately inconclusive. To address this
issue, we conduct a rigorous analysis and comparison of bias detection methods
for contextual language models. Our results show that minor design and
implementation decisions (or errors) have a substantial and often significant
impact on the derived bias scores. Overall, we find the state of the field to
be both worse than previously acknowledged due to systematic and propagated
errors in implementations, yet better than anticipated since divergent results
in the literature homogenize after accounting for implementation errors. Based
on our findings, we conclude with a discussion of paths towards more robust and
consistent bias detection methods.
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