Mitigate Gender Bias in Construction: Fusion of Deep Reinforcement Learning-Based Contract Theory and Blockchain

Zijun Zhan, Yaxian Dong, Daniel Mawunyo Doe, Yuqing Hu, Shuai Li, Shaohua Cao, Wei Li, Zhu Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE International Conference on Blockchain, Blockchain 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages86-91
Number of pages6
ISBN (Electronic)9798350319293
DOIs
StatePublished - 2023
Event6th IEEE International Conference on Blockchain, Blockchain 2023 - Hainan, China
Duration: Dec 17 2023Dec 21 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Blockchain, Blockchain 2023

Conference

Conference6th IEEE International Conference on Blockchain, Blockchain 2023
Country/TerritoryChina
CityHainan
Period12/17/2312/21/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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