Publication
Title
Measuring fairness with biased rulers : a survey on quantifying biases in pretrained language models
Author
Abstract
An increasing awareness of biased patterns in natural language processing resources, like BERT, has motivated many metrics to quantify `bias' and `fairness'. But comparing the results of different metrics and the works that evaluate with such metrics remains difficult, if not outright impossible. We survey the existing literature on fairness metrics for pretrained language models and experimentally evaluate compatibility, including both biases in language models as in their downstream tasks. We do this by a mixture of traditional literature survey and correlation analysis, as well as by running empirical evaluations. We find that many metrics are not compatible and highly depend on (i) templates, (ii) attribute and target seeds and (iii) the choice of embeddings. These results indicate that fairness or bias evaluation remains challenging for contextualized language models, if not at least highly subjective. To improve future comparisons and fairness evaluations, we recommend avoiding embedding-based metrics and focusing on fairness evaluations in downstream tasks.
Language
English
Publication
arXiv , 2021
DOI
10.48550/ARXIV.2112.07447
Volume/pages
15 p.
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
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Affiliation
Publications with a UAntwerp address
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Creation 16.10.2023
Last edited 04.03.2024
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