Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling

Flavia Salutari; Jerome Ramos; Hossein A. Rahmani; Leonardo Linguaglossa; Aldo Lipani

doi:10.1007/978-3-031-33374-3_42

Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling

Flavia Salutari
, Jerome Ramos
, Hossein A. Rahmani
, Leonardo Linguaglossa
, Aldo Lipani

University College London
Telecom Paris

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

6 Citations (Scopus)

Résumé

In recent years, groundbreaking transformer-based language models (LMs) have made tremendous advances in natural language processing (NLP) tasks. However, the measurement of their fairness with respect to different social groups still remains unsolved. In this paper, we propose and thoroughly validate an evaluation technique to assess the quality and bias of language model predictions on transcripts of both spoken African American English (AAE) and Spoken American English (SAE). Our analysis reveals the presence of a bias towards SAE encoded by state-of-the-art LMs such as BERT and DistilBERT and a lower bias in distilled LMs. We also observe a bias towards AAE in RoBERTa and BART. Additionally, we show evidence that this disparity is present across all the LMs when we only consider the grammar and the syntax specific to AAE.

langue originale	Anglais
titre	Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings
rédacteurs en chef	Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	532-543
Nombre de pages	12
ISBN (imprimé)	9783031333736
Les DOIs	https://doi.org/10.1007/978-3-031-33374-3_42
état	Publié - 1 janv. 2023
Evénement	27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 - Hybrid, Osaka, Japon Durée: 25 mai 2023 → 28 mai 2023

Série de publications

Nom	Lecture Notes in Computer Science
Volume	13935 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
Pays/Territoire	Japon
La ville	Hybrid, Osaka
période	25/05/23 → 28/05/23

Accès au document

10.1007/978-3-031-33374-3_42

Contient cette citation

Salutari, F., Ramos, J., Rahmani, H. A., Linguaglossa, L., & Lipani, A. (2023). Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling. Dans H. Kashima, T. Ide, & W.-C. Peng (eds.), Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings (p. 532-543). (Lecture Notes in Computer Science; Vol 13935 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-33374-3_42

Salutari, Flavia ; Ramos, Jerome ; Rahmani, Hossein A. et al. / Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling. Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Editeur / Hisashi Kashima ; Tsuyoshi Ide ; Wen-Chih Peng. Springer Science and Business Media Deutschland GmbH, 2023. p. 532-543 (Lecture Notes in Computer Science).

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title = "Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling",

abstract = "In recent years, groundbreaking transformer-based language models (LMs) have made tremendous advances in natural language processing (NLP) tasks. However, the measurement of their fairness with respect to different social groups still remains unsolved. In this paper, we propose and thoroughly validate an evaluation technique to assess the quality and bias of language model predictions on transcripts of both spoken African American English (AAE) and Spoken American English (SAE). Our analysis reveals the presence of a bias towards SAE encoded by state-of-the-art LMs such as BERT and DistilBERT and a lower bias in distilled LMs. We also observe a bias towards AAE in RoBERTa and BART. Additionally, we show evidence that this disparity is present across all the LMs when we only consider the grammar and the syntax specific to AAE.",

keywords = "Bias and Fairness, Evaluation, Language Model, Transformers",

author = "Flavia Salutari and Jerome Ramos and Rahmani, \{Hossein A.\} and Leonardo Linguaglossa and Aldo Lipani",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 ; Conference date: 25-05-2023 Through 28-05-2023",

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Salutari, F, Ramos, J, Rahmani, HA, Linguaglossa, L & Lipani, A 2023, Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling. Dans H Kashima, T Ide & W-C Peng (eds), Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Lecture Notes in Computer Science, VOL. 13935 LNCS, Springer Science and Business Media Deutschland GmbH, p. 532-543, 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Hybrid, Osaka, Japon, 25/05/23. https://doi.org/10.1007/978-3-031-33374-3_42

Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling. / Salutari, Flavia; Ramos, Jerome; Rahmani, Hossein A. et al.
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Ed. / Hisashi Kashima; Tsuyoshi Ide; Wen-Chih Peng. Springer Science and Business Media Deutschland GmbH, 2023. p. 532-543 (Lecture Notes in Computer Science; Vol 13935 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling

AU - Salutari, Flavia

AU - Ramos, Jerome

AU - Rahmani, Hossein A.

AU - Linguaglossa, Leonardo

AU - Lipani, Aldo

PY - 2023/1/1

Y1 - 2023/1/1

N2 - In recent years, groundbreaking transformer-based language models (LMs) have made tremendous advances in natural language processing (NLP) tasks. However, the measurement of their fairness with respect to different social groups still remains unsolved. In this paper, we propose and thoroughly validate an evaluation technique to assess the quality and bias of language model predictions on transcripts of both spoken African American English (AAE) and Spoken American English (SAE). Our analysis reveals the presence of a bias towards SAE encoded by state-of-the-art LMs such as BERT and DistilBERT and a lower bias in distilled LMs. We also observe a bias towards AAE in RoBERTa and BART. Additionally, we show evidence that this disparity is present across all the LMs when we only consider the grammar and the syntax specific to AAE.

AB - In recent years, groundbreaking transformer-based language models (LMs) have made tremendous advances in natural language processing (NLP) tasks. However, the measurement of their fairness with respect to different social groups still remains unsolved. In this paper, we propose and thoroughly validate an evaluation technique to assess the quality and bias of language model predictions on transcripts of both spoken African American English (AAE) and Spoken American English (SAE). Our analysis reveals the presence of a bias towards SAE encoded by state-of-the-art LMs such as BERT and DistilBERT and a lower bias in distilled LMs. We also observe a bias towards AAE in RoBERTa and BART. Additionally, we show evidence that this disparity is present across all the LMs when we only consider the grammar and the syntax specific to AAE.

KW - Bias and Fairness

KW - Evaluation

KW - Language Model

KW - Transformers

U2 - 10.1007/978-3-031-33374-3_42

DO - 10.1007/978-3-031-33374-3_42

M3 - Conference contribution

AN - SCOPUS:85173561010

SN - 9783031333736

T3 - Lecture Notes in Computer Science

SP - 532

EP - 543

BT - Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings

A2 - Kashima, Hisashi

A2 - Ide, Tsuyoshi

A2 - Peng, Wen-Chih

PB - Springer Science and Business Media Deutschland GmbH

T2 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023

Y2 - 25 May 2023 through 28 May 2023

ER -

Salutari F, Ramos J, Rahmani HA, Linguaglossa L, Lipani A. Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling. Dans Kashima H, Ide T, Peng WC, rédacteurs en chef, Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 532-543. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-33374-3_42

Quantifying the Bias of Transformer-Based Language Models for African American English in Masked Language Modeling

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