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Authors: Sarah Schröder ; Alexander Schulz ; Philip Kenneweg and Barbara Hammer

Affiliation: CITEC, Bielefeld University, Inspiration 1, 33619 Bielefeld, Germany

Keyword(s): Intrinsic Bias Measures, Pretrained Language Models, Template-Based Evaluation.

Abstract: While text embeddings have become the state-of-the-art in many natural language processing applications, the presence of bias that such models often learn from training data can become a serious problem. As a reaction, a large variety of measures for detecting bias has been proposed. However, an extensive comparison between them does not exists so far. We aim to close this gap for the class of intrinsic bias measures in the context of pretrained language models and propose an experimental setup which allows a fair comparison by using a large set of templates for each bias measure. Our setup is based on the idea of simulating pretraining on a set of differently biased corpora, thereby obtaining a ground truth for the present bias. This allows us to evaluate in how far bias is detected by different measures and also enables to judge the robustness of bias scores.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Schröder, S.; Schulz, A.; Kenneweg, P. and Hammer, B. (2023). So Can We Use Intrinsic Bias Measures or Not?. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 403-410. DOI: 10.5220/0011693700003411

@conference{icpram23,
author={Sarah Schröder. and Alexander Schulz. and Philip Kenneweg. and Barbara Hammer.},
title={So Can We Use Intrinsic Bias Measures or Not?},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011693700003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - So Can We Use Intrinsic Bias Measures or Not?
SN - 978-989-758-626-2
IS - 2184-4313
AU - Schröder, S.
AU - Schulz, A.
AU - Kenneweg, P.
AU - Hammer, B.
PY - 2023
SP - 403
EP - 410
DO - 10.5220/0011693700003411
PB - SciTePress