Embedding Fairness into the AI-Based Talent Recruitment Systems：The Perspective of Environment Cycle and Knowledge Cycle

Authors

Yuqing Zhao, Tianjin UniversityFollow
Xi Zhang, Tianjin UniversityFollow
Xinlin Tang, Florida State UniversityFollow
Chuan Qin, Baidu Talent Intelligence CenterFollow
Hengshu Zhu, Baidu Talent Intelligence CenterFollow

Paper Type

RIP

Paper Number

423

Abstract

Companies are increasingly adopting artificial intelligence (AI) technology to manage talent recruitment because of its potential to make recruitment easier, more accurate, and ef icient. Despite its benefits, AI-based recruitment system can raise fairness concerns, such as the gender discrimination scandal of Amazon’s AI-based recruiting system. Part of the problem is that no comprehensive framework has been developed to systematically address the fairness issue in AI-based recruitment system. This study intends to address this research void by embedding the fairness mechanism into the system development process using design science research method. Building on a systematic literature review on the source of fairness in AI- based recruitment system, this study has the potential to generate important theoretical and practical implications for designing and implementing a fair AI-based recruitment system.

Recommended Citation

Zhao, Yuqing; Zhang, Xi; Tang, Xinlin; Qin, Chuan; and Zhu, Hengshu, "Embedding Fairness into the AI-Based Talent Recruitment Systems：The Perspective of Environment Cycle and Knowledge Cycle" (2021). PACIS 2021 Proceedings. 15.
https://aisel.aisnet.org/pacis2021/15