Uncovering Latent Biases in Text: Method and Application to Peer Review
This work addresses the challenge of measuring text-based biases in real-world settings like peer review, providing a method to infer ground truth biases, though it is incremental as it builds on existing bias quantification concepts.
The authors tackled the problem of quantifying biases in text due to subgroup membership visibility by developing a nonparametric framework and applied it to peer reviews from a machine learning conference before and after adopting a double-blind policy, showing accurate detection of biases without access to review ratings.
Quantifying systematic disparities in numerical quantities such as employment rates and wages between population subgroups provides compelling evidence for the existence of societal biases. However, biases in the text written for members of different subgroups (such as in recommendation letters for male and non-male candidates), though widely reported anecdotally, remain challenging to quantify. In this work, we introduce a novel framework to quantify bias in text caused by the visibility of subgroup membership indicators. We develop a nonparametric estimation and inference procedure to estimate this bias. We then formalize an identification strategy to causally link the estimated bias to the visibility of subgroup membership indicators, provided observations from time periods both before and after an identity-hiding policy change. We identify an application wherein "ground truth" bias can be inferred to evaluate our framework, instead of relying on synthetic or secondary data. Specifically, we apply our framework to quantify biases in the text of peer reviews from a reputed machine learning conference before and after the conference adopted a double-blind reviewing policy. We show evidence of biases in the review ratings that serves as "ground truth", and show that our proposed framework accurately detects these biases from the review text without having access to the review ratings.