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Algorithmic Bias In Current Hiring Practices: An Ethical Examination

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  • Drago? BÃŽGU
  • Mihail-Valentin CERNEA

Abstract

This paper will explore the ethical consequences of using machine learning algorithms in hiring decisions, focusing on the risk of discriminating groups of people based on unjust criteria. The first section of the paper is concerned with describing the automated processes involved in current hiring practices and three sources of possible unjust discrimination: (i) the defined outputs of the algorithms involved; (ii) the way in which the predicted work performance is understood by managers; (iii) statistical correlations could be biased against certain groups of people, precluding the evaluation of individuals based on their own work performance. The second section of the paper offers a comparison between traditional cases of discrimination and this new kind of algorithmic discrimination and three solutions for mitigating the risk of discrimination in automated hiring practices, i.e., transparency, careful testing for biases that could have ingrained themselves in the software used in the hiring process, and by ensuring that the final decision is made by a human and not a machine.

Suggested Citation

  • Drago? BÃŽGU & Mihail-Valentin CERNEA, 2019. "Algorithmic Bias In Current Hiring Practices: An Ethical Examination," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 1068-1073, November.
    • Handle: RePEc:rom:mancon:v:13:y:2019:i:1:p:1068-1073
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    References listed on IDEAS

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    1. Jenifer Winter, 2015. "Algorithmic Discrimination: Big Data Analytics and the Future of the Internet," Public Administration and Information Technology, in: Jenifer Winter & Ryota Ono (ed.), The Future Internet, edition 1, chapter 0, pages 125-140, Springer.
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    Cited by:

    1. Anna Lena Hunkenschroer & Christoph Luetge, 2022. "Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda," Journal of Business Ethics, Springer, vol. 178(4), pages 977-1007, July.

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