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Win, Khin Sandar (2025) Beyond Bias with Q-FRE: Benchmarking a Quantum Machine Learning Framework for Fair Recruitment. Masters thesis, Dublin, National College of Ireland.
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Abstract
Artificial Intelligence has significantly improved the efficiency of modern recruitment systems. Despite these advances, classical machine learning algorithms often perpetuate bias towards underrepresented groups, particularly in the context of imbalanced datasets. This study investigates whether Quantum Machine Learning (QML), through properties such as superposition and entanglement, can enhance fairness over classical methods.
A rigorous system of comparative benchmarking, the Quantum-Fairness Recruitment Engine (Q-FRE), was designed to evaluate six architectures, with three classical (Logistic Regression, SVM, KNN) and three quantum (QSVM, VQC, QNN)-based machine learning algorithms, using real HR data (n=1,470).
The results indicate a notable divergence between classical and quantum variational architectures. Classical models were highly accurate (84.7%), but had disastrous fairness errors, where 0% of minority high-performers were identified. This failure was repeated by Quantum SVM. On the other hand, the Variational Quantum Classifier (VQC) and Quantum Neural Network (QNN) were able to identify 29% of minority high-performers with a Disparate Impact Ratio of 0.82, which is legal for fairness.
This research demonstrates empirical evidence of a quantifiable Fairness-Accuracy Trade-off: to gain a 29% increase in minority inclusion required a 16% sacrifice of overall accuracy. The results indicate that parameterized quantum circuits provide a possible, though computationally costly, route toward for achieving regulatory compliance in automated hiring.
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