Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements

Conrad Borchers, Dalia Gala, Benjamin Gilburt, Eduard Oravkin, Wilfried Bounsi, Yuki M Asano, Hannah Kirk


Abstract
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements. We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements. We then evaluate prompt-engineering and fine-tuning as debiasing methods. We find that prompt-engineering with diversity-encouraging prompts gives no significant improvement to bias, nor realism. Conversely, fine-tuning, especially on unbiased real advertisements, can improve realism and reduce bias.
Anthology ID:
2022.gebnlp-1.22
Volume:
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–224
Language:
URL:
https://aclanthology.org/2022.gebnlp-1.22
DOI:
10.18653/v1/2022.gebnlp-1.22
Bibkey:
Cite (ACL):
Conrad Borchers, Dalia Gala, Benjamin Gilburt, Eduard Oravkin, Wilfried Bounsi, Yuki M Asano, and Hannah Kirk. 2022. Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 212–224, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements (Borchers et al., GeBNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.gebnlp-1.22.pdf
Video:
 https://aclanthology.org/2022.gebnlp-1.22.mp4
Code
 oxai/gpt3-jobadvert-bias