TY - GEN
T1 - Mitigate Gender Bias in Construction
T2 - 6th IEEE International Conference on Blockchain, Blockchain 2023
AU - Zhan, Zijun
AU - Dong, Yaxian
AU - Doe, Daniel Mawunyo
AU - Hu, Yuqing
AU - Li, Shuai
AU - Cao, Shaohua
AU - Li, Wei
AU - Han, Zhu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the remarkable progress in teleoperation, physical fitness-based gender bias has become negligible within the construction sector. Nonetheless, the labor market remains male-dominated, posing tremendous unfairness toward females. In light of this, we developed a two-phase recruitment framework that utilizes blockchain, zero-knowledge proofs (ZKPs), deep reinforcement learning (DRL), and contract theory, aiming to enhance fairness, transparency, and automation. First, we devised a resume screening approach independent of gender to ensure fairness and alleviate gender bias in candidate assessment, by leveraging blockchain and ZKPs. In the second phase, we introduce a recruitment process that combines blockchain and DRL-based contract theory. This integration successfully mitigates gender bias that may arise from the self-disclosure property of contract theory. To evaluate the effectiveness of our proposed approach, we conducted comprehensive simulations from various dimensions. The results demonstrated the robustness and superiority of our method.
AB - With the remarkable progress in teleoperation, physical fitness-based gender bias has become negligible within the construction sector. Nonetheless, the labor market remains male-dominated, posing tremendous unfairness toward females. In light of this, we developed a two-phase recruitment framework that utilizes blockchain, zero-knowledge proofs (ZKPs), deep reinforcement learning (DRL), and contract theory, aiming to enhance fairness, transparency, and automation. First, we devised a resume screening approach independent of gender to ensure fairness and alleviate gender bias in candidate assessment, by leveraging blockchain and ZKPs. In the second phase, we introduce a recruitment process that combines blockchain and DRL-based contract theory. This integration successfully mitigates gender bias that may arise from the self-disclosure property of contract theory. To evaluate the effectiveness of our proposed approach, we conducted comprehensive simulations from various dimensions. The results demonstrated the robustness and superiority of our method.
UR - http://www.scopus.com/inward/record.url?scp=85185563849&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85185563849&partnerID=8YFLogxK
U2 - 10.1109/Blockchain60715.2023.00023
DO - 10.1109/Blockchain60715.2023.00023
M3 - Conference contribution
AN - SCOPUS:85185563849
T3 - Proceedings - 2023 IEEE International Conference on Blockchain, Blockchain 2023
SP - 86
EP - 91
BT - Proceedings - 2023 IEEE International Conference on Blockchain, Blockchain 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 December 2023 through 21 December 2023
ER -