Bias and Fairness in Automated Recruitment and Employee Selection Systems

Aida A. Kusairi

Bias and Fairness in Automated Recruitment and Employee Selection Systems

Author(s)	Dr. Aida A. Kusairi
Country	United States
Abstract	Automated recruitment and employee selection systems are increasingly used to identify applicants, screen résumés, administer assessments, rank candidates, and support hiring decisions. Their growing use has created expectations that data-driven decision-making can improve consistency, efficiency, and objectivity in personnel selection. At the same time, algorithmic systems may reproduce or intensify existing inequalities when they learn from historically biased employment data, rely on inappropriate proxy variables, use poorly validated outcome measures, or produce systematically different error rates across applicant groups. This study examines the sources, manifestations, and mitigation of bias in automated recruitment through a conceptual and simulation-based analytical framework. The analysis considers four stages of the recruitment process: candidate sourcing, résumé screening, pre-employment assessment, and final shortlisting. A simulated selection-rate ratio is used to illustrate how an unmitigated automated system may produce unequal outcomes and how a fairness-governed system may reduce disparities through dataset auditing, job-related feature selection, subgroup validation, fairness testing, explainability, and structured human review. The simulated results indicate meaningful improvement across all four stages after governance mechanisms are introduced, although the study does not interpret mathematical parity as sufficient evidence of substantive fairness. The paper argues that fair automated recruitment requires a sociotechnical governance approach in which technical validation is combined with job relevance, procedural transparency, accessibility, applicant safeguards, human accountability, and continuous post-deployment monitoring. Automated selection should therefore function as accountable decision support rather than an unquestionable substitute for professional judgment.
Keywords	: algorithmic bias, automated recruitment, employee selection, artificial intelligence, hiring algorithms, algorithmic fairness, recruitment analytics, human oversight.
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-04-08

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American International Journal of Research and Innovation

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Bias and Fairness in Automated Recruitment and Employee Selection Systems

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