Quantum ML models audited for fairness in medical imaging

Researchers have conducted a fairness audit on quantum machine learning (QML) models used for medical imaging, finding that while QML can achieve competitive performance, it may still exhibit disparities across sensitive subgroups. The study evaluated classical and quantum classifiers on dermatological and chest X-ray datasets, employing mitigation techniques like Equalized Odds regularization and Gradient Reversal Learning. Results indicate that fairness improvements can sometimes come at the cost of overall performance, and the effectiveness of mitigation strategies is dependent on the specific model and dataset. The paper concludes that empirical subgroup-aware evaluation is crucial for QML in medical applications. AI

IMPACT Highlights the need for rigorous fairness evaluations in quantum machine learning applications, particularly in sensitive domains like healthcare.

RANK_REASON The cluster contains a research paper detailing an empirical audit of quantum machine learning models for fairness in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• CheXpert
• Equalized Odds
• Fitzpatrick
• Gradient Reversal Learning
• medical imaging
• multilayer perceptron
• QCNN
• QSVM
• Quantum Machine Learning
• ResNet-18
• support vector machine

• paper
• safety

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