On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
This work addresses fairness evaluation in NLP, providing insights for researchers and practitioners, but it is incremental as it builds on existing metrics without introducing new methods.
The paper tackled the problem of evaluating fairness in contextualized language models by studying the correlation between intrinsic and extrinsic metrics across 19 models, finding that these metrics do not necessarily correlate even after correcting for various factors.
Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) \emph{extrinsic metrics} for evaluating fairness in downstream applications and 2) \emph{intrinsic metrics} for estimating fairness in upstream contextualized language representation models. In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized language models. We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics. %al