Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance

Wambsganss, Thiemo; Su, Xiaotian; Swamy, Vinitra; Neshaei, Seyed; Rietsche, Roman; Käser, Tanja

Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance

URI

https://arbor.bfh.ch/handle/arbor/36676

Version

Published

Date Issued

2023-12-06

Author(s)

Wambsganss, Thiemo

Su, Xiaotian

Swamy, Vinitra

Neshaei, Seyed

Rietsche, Roman

Käser, Tanja

Type

Conference Paper

Language

English

Subjects

T Technology (General)

Publisher URL

https://2023.emnlp.org/

Organization

Institut Digital Technology Management

Wirtschaft

Conference

Findings of the Association for Computational Linguistics: EMNLP 2023

Citation apa

Wambsganss, T., Su, X., Swamy, V., Neshaei, S., Rietsche, R., & Käser, T. (2023). Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance. Findings of the Association for Computational Linguistics: EMNLP 2023. https://arbor.bfh.ch/handle/arbor/36676