A Bayesian approach to uncertainty in word embedding bias estimation
This addresses the issue of false confidence in bias estimation for researchers and practitioners in NLP, offering a more nuanced method, though it is incremental as it builds on existing bias measurement frameworks.
The paper tackles the problem of overconfidence in existing single-number metrics for word embedding bias by showing they can produce similar results even under a null model lacking bias. It proposes a Bayesian hierarchical modeling approach that reveals a more complex bias landscape across Religion, Gender, and Race word lists in embeddings like Google, Glove, and Reddit.
Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and employing bootstrapping techniques with low sample sizes. We show that similar results can be easily obtained using such methods even if the data are generated by a null model lacking the intended bias. Consequently, we argue that this approach generates false confidence. To address this issue, we propose a Bayesian alternative: hierarchical Bayesian modeling, which enables a more uncertainty-sensitive inspection of bias in word embeddings at different levels of granularity. To showcase our method, we apply it to Religion, Gender, and Race word lists from the original research, together with our control neutral word lists. We deploy the method using Google, Glove, and Reddit embeddings. Further, we utilize our approach to evaluate a debiasing technique applied to Reddit word embedding. Our findings reveal a more complex landscape than suggested by the proponents of single-number metrics. The datasets and source code for the paper are publicly available.