| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-12-26 |
| タイトル |
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タイトル |
MAG-BERT-ARL for Fair Automated Video Interview Assessment |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
Interviews |
| キーワード |
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主題Scheme |
Other |
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主題 |
Acoustics |
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主題Scheme |
Other |
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主題 |
Visualization |
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主題Scheme |
Other |
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主題 |
Feature extraction |
| キーワード |
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主題Scheme |
Other |
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主題 |
Transformers |
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主題Scheme |
Other |
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主題 |
Accuracy |
| キーワード |
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主題Scheme |
Other |
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主題 |
Training |
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主題Scheme |
Other |
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主題 |
Adversarial machine learning |
| キーワード |
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主題Scheme |
Other |
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主題 |
Measurement |
| キーワード |
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主題Scheme |
Other |
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主題 |
Logic gates |
| キーワード |
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主題Scheme |
Other |
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主題 |
Videos |
| キーワード |
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主題Scheme |
Other |
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主題 |
Automatic video interview assessment |
| キーワード |
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主題Scheme |
Other |
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主題 |
fairness |
| キーワード |
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主題Scheme |
Other |
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主題 |
model interpretability |
| キーワード |
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主題Scheme |
Other |
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主題 |
adversarial learning |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Putra, Bimasena
Azizah, Kurniawati
Olivia, Mawalim Candy
Akmal, Hanif Ikhlasul
Sakti, Sakriani
Wee Leong, Chee
Okada, Shogo
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Potential biases within automated video interview assessment algorithms may disadvantage specific demographics due to the collection of sensitive attributes, which are regulated by the General Data Protection Regulation (GDPR). To mitigate these fairness concerns, this research introduces MAG-BERT-ARL, an automated video interview assessment system that eliminates reliance on sensitive attributes. MAG-BERT-ARL integrates Multimodal Adaptation Gate and Bidirectional Encoder Representations from Transformers (MAG-BERT) model with the Adversarially Reweighted Learning (ARL). This integration aims to improve the performance of underrepresented groups by promoting Rawlsian Max-Min Fairness. Through experiments on the Educational Testing Service (ETS) and First Impressions (FI) datasets, the proposed method demonstrates its effectiveness in optimizing model performance (increasing Pearson correlation coefficient up to 0.17 in the FI dataset and precision up to 0.39 in the ETS dataset) and fairness (reducing equal accuracy up to 0.11 in the ETS dataset). The findings underscore the significance of integrating fairness-enhancing techniques like ARL and highlight the impact of incorporating nonverbal cues on hiring decisions. |
| 書誌情報 |
en : IEEE Access
巻 12,
p. 145188-145205,
ページ数 18,
発行日 2024-10-03
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| 出版者 |
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出版者 |
IEEE |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2169-3536 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1109/ACCESS.2024.3473314 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/abstract/document/10704666 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Faculty of Computer Science, Universitas Indonesia |
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研究課題番号 |
NKB-7/UN2.F11.D/HKP.05.00/2024 |
| 助成情報 |
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助成機関名 |
Japan Science and Technology Agency (JST) |
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研究課題名 |
Sakura Science Program |
