@inproceedings{hemamou-coleman-2022-delivering,
title = "Delivering Fairness in Human Resources {AI}: Mutual Information to the Rescue",
author = "Hemamou, Leo and
Coleman, William",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.64",
doi = "10.18653/v1/2022.aacl-main.64",
pages = "867--882",
abstract = "Automatic language processing is used frequently in the Human Resources (HR) sector for automated candidate sourcing and evaluation of resumes. These models often use pre-trained language models where it is difficult to know if possible biases exist. Recently, Mutual Information (MI) methods have demonstrated notable performance in obtaining representations agnostic to sensitive variables such as gender or ethnicity. However, accessing these variables can sometimes be challenging, and their use is prohibited in some jurisdictions. These factors can make detecting and mitigating biases challenging. In this context, we propose to minimize the MI between a candidate{'}s name and a latent representation of their CV or short biography. This method may mitigate bias from sensitive variables without requiring the collection of these variables. We evaluate this methodology by first projecting the name representation into a smaller space to prevent potential MI minimization problems in high dimensions.",
}
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<abstract>Automatic language processing is used frequently in the Human Resources (HR) sector for automated candidate sourcing and evaluation of resumes. These models often use pre-trained language models where it is difficult to know if possible biases exist. Recently, Mutual Information (MI) methods have demonstrated notable performance in obtaining representations agnostic to sensitive variables such as gender or ethnicity. However, accessing these variables can sometimes be challenging, and their use is prohibited in some jurisdictions. These factors can make detecting and mitigating biases challenging. In this context, we propose to minimize the MI between a candidate’s name and a latent representation of their CV or short biography. This method may mitigate bias from sensitive variables without requiring the collection of these variables. We evaluate this methodology by first projecting the name representation into a smaller space to prevent potential MI minimization problems in high dimensions.</abstract>
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%0 Conference Proceedings
%T Delivering Fairness in Human Resources AI: Mutual Information to the Rescue
%A Hemamou, Leo
%A Coleman, William
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F hemamou-coleman-2022-delivering
%X Automatic language processing is used frequently in the Human Resources (HR) sector for automated candidate sourcing and evaluation of resumes. These models often use pre-trained language models where it is difficult to know if possible biases exist. Recently, Mutual Information (MI) methods have demonstrated notable performance in obtaining representations agnostic to sensitive variables such as gender or ethnicity. However, accessing these variables can sometimes be challenging, and their use is prohibited in some jurisdictions. These factors can make detecting and mitigating biases challenging. In this context, we propose to minimize the MI between a candidate’s name and a latent representation of their CV or short biography. This method may mitigate bias from sensitive variables without requiring the collection of these variables. We evaluate this methodology by first projecting the name representation into a smaller space to prevent potential MI minimization problems in high dimensions.
%R 10.18653/v1/2022.aacl-main.64
%U https://aclanthology.org/2022.aacl-main.64
%U https://doi.org/10.18653/v1/2022.aacl-main.64
%P 867-882
Markdown (Informal)
[Delivering Fairness in Human Resources AI: Mutual Information to the Rescue](https://aclanthology.org/2022.aacl-main.64) (Hemamou & Coleman, AACL-IJCNLP 2022)
ACL
- Leo Hemamou and William Coleman. 2022. Delivering Fairness in Human Resources AI: Mutual Information to the Rescue. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 867–882, Online only. Association for Computational Linguistics.