SOS: Systematic Offensive Stereotyping Bias in Word Embeddings
Elsafoury, Fatma; Wilson, Steven R.; Katsigiannis, Stamos; Ramzan, Naeem
Steven R. Wilson
Dr Stamos Katsigiannis email@example.com
Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms. In this [id=stk]work, we introduce a quantitative measure of the SOS bias, [id=stk]validate it in the most commonly used word embeddings, and investigate if it explains the performance of different word embeddings on the task of hate speech detection. Results show that SOS bias exists in almost all examined word embeddings and that [id=stk]the proposed SOS bias metric correlates positively with the statistics of published surveys on online extremism. We also show that the [id=stk]proposed metric reveals distinct information [id=stk]compared to established social bias metrics. However, we do not find evidence that SOS bias explains the performance of hate speech detection models based on the different word embeddings.
Elsafoury, F., Wilson, S. R., Katsigiannis, S., & Ramzan, N. (2022). SOS: Systematic Offensive Stereotyping Bias in Word Embeddings.
|Conference Name||29th International Conference on Computational Linguistics (COLING 2022)|
|Conference Location||Gyeongju, Republic of Korea|
|Start Date||Oct 12, 2022|
|End Date||Oct 17, 2022|
|Acceptance Date||Aug 16, 2022|
|Deposit Date||Aug 19, 2022|
|Publicly Available Date||Apr 28, 2023|
Published Conference Proceeding
Publisher Licence URL
© 1963–2023 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
You might also like
Towards Automatic Tutoring of Custom Student-Stated Math Word Problems
Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems
Multi-modal lung ultrasound image classification by fusing image-based features and probe information
Automated Detection of Substance-Use Status and Related Information from Clinical Text