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Ethics and discrimination in artificial intelligence-enabled recruitment practices

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  • Zhisheng Chen

    (Nanjing University of Aeronautics and Astronautics)

Abstract

This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents’ experiences and perceptions of AI-driven recruitment applications and discrimination.

Suggested Citation

  • Zhisheng Chen, 2023. "Ethics and discrimination in artificial intelligence-enabled recruitment practices," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02079-x
    DOI: 10.1057/s41599-023-02079-x
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    References listed on IDEAS

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    1. David L. Dickinson & Ronald L. Oaxaca, 2009. "Statistical Discrimination in Labor Markets: An Experimental Analysis," Southern Economic Journal, John Wiley & Sons, vol. 76(1), pages 16-31, July.
    2. Lundberg, Shelly J & Startz, Richard, 1983. "Private Discrimination and Social Intervention in Competitive Labor Markets," American Economic Review, American Economic Association, vol. 73(3), pages 340-347, June.
    3. Kanchana Ruwanpura, 2008. "Multiple identities, multiple-discrimination: A critical review," Feminist Economics, Taylor & Francis Journals, vol. 14(3), pages 77-105.
    4. David L. Dickinson & Ronald L. Oaxaca, 2009. "Statistical Discrimination in Labor Markets: An Experimental Analysis," Southern Economic Journal, John Wiley & Sons, vol. 76(1), pages 16-31, July.
    5. Bilal HMOUD & Varallyai LASZLO, 2019. "Will Artificial Intelligence Take Over Humanresources Recruitment And Selection?," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 13, pages 21-30, July.
    6. repec:eee:labchp:v:1:y:1986:i:c:p:693-785 is not listed on IDEAS
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    Cited by:

    1. Athanasios Polyportis & Nikolaos Pahos, 2024. "Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    2. Yang Shen & Xiuwu Zhang, 2024. "The impact of artificial intelligence on employment: the role of virtual agglomeration," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.

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