Responsible Algorithm Design for Reducing Bias in Automated Recruitment Platforms

Marcus Lee

Responsible Algorithm Design for Reducing Bias in Automated Recruitment Platforms

Author(s)	Dr. Marcus Lee
Country	United States
Abstract	Automated recruitment platforms increasingly support résumé screening, candidate ranking, assessment, interview scheduling, and employment-related recommendations. Although algorithmic systems can process large applicant pools efficiently and apply standardized criteria, they may also reproduce historical inequalities when trained on previous hiring outcomes, indirectly infer protected characteristics through proxy variables, or optimize performance without evaluating subgroup error patterns. This study develops a Responsible Algorithm Design Framework for Bias-Resilient Recruitment (RAD-BRR), integrating job-related feature selection, proxy-variable screening, representative validation, fairness measurement, constrained model development, explainability, human review, and continuous post-deployment auditing. A design-science methodology was combined with structured evidence mapping and reproducible synthetic simulation. The analytical experiment included 5,000 synthetic applicant profiles and distinguished protected-group information used exclusively for fairness auditing from features permitted for candidate prediction. A historical-pattern baseline model incorporated variables reflecting unequal opportunity structures, whereas the responsible configuration relied primarily on demonstrated skills, work-sample performance, and relevant experience. Both systems were evaluated using accuracy, balanced accuracy, selection rates, selection-rate ratio, equal-opportunity difference, and false-negative-rate disparity. On the held-out simulation sample, the historical-pattern model produced a selection-rate ratio of 0.576 and an equal-opportunity gap of 0.262. The responsible design increased the selection-rate ratio to 0.966 while reducing the equal-opportunity and false-negative-rate gaps to 0.011. Balanced accuracy simultaneously increased from 79.6% to 83.6%, indicating that bias reduction did not require sacrificing predictive utility under the assumptions of the simulation. The study argues that responsible recruitment cannot be achieved merely by removing explicit demographic variables. Effective bias mitigation requires examination of training labels, proxy information, job relevance, subgroup errors, decision thresholds, explanations, accessibility, and human accountability across the recruitment lifecycle. The proposed framework offers a practical foundation for recruitment systems designed to enhance efficiency without institutionalizing historical discrimination.
Keywords	: algorithmic recruitment; responsible algorithm design; hiring bias; fairness-aware machine learning; automated employment decision tools; disparate impact; equal opportunity; recruitment analytics; algorithmic auditing; responsible artificial intelligence
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2020
Published On	2020-01-30

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American Journal of Advanced Multidisciplinary Innovation and Research

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Responsible Algorithm Design for Reducing Bias in Automated Recruitment Platforms

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