Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 10:15 AM
End Date
1-4-2026 11:30 AM
Description
Bias in multimodal AI systems that jointly process image and text inputs creates measurable risks in sensitive deployment contexts including public health, financial services, and automated hiring. Classical detection approaches face a fundamental architectural limitation in that they cannot efficiently model intersectional bias. Intersectional bias emerges from the nonlinear interaction of multiple protected attributes simultaneously across visual and linguistic modalities. This paper introduces QBiasNet, a hybrid quantum-classical system that encodes cross-modal CLIP embeddings into an 8-qubit Variational Quantum Circuit (VQC) implemented in PennyLane. By exploiting quantum entanglement, the VQC learns high-order feature correlations that linear probes and shallow neural networks systematically miss. Evaluated on a 12,000-sample benchmark merging the FairFace and WinoBias datasets, QBiasNet achieves 91.4% bias detection accuracy and reduces the intersectional bias false negative rate by 33% relative to the strongest classical baseline. An entanglement ablation study confirms that the structural properties of the VQC are responsible for the observed performance advantage, not its parameter count. These findings suggest that quantum-enhanced classifiers are a practical and theoretically grounded tool for next-generation AI governance and regulatory compliance auditing.
QBiasNet: Quantum-Enhanced Variational Classifiers for Ethical Bias Detection in Multimodal AI Models
Kennesaw, Georgia
Bias in multimodal AI systems that jointly process image and text inputs creates measurable risks in sensitive deployment contexts including public health, financial services, and automated hiring. Classical detection approaches face a fundamental architectural limitation in that they cannot efficiently model intersectional bias. Intersectional bias emerges from the nonlinear interaction of multiple protected attributes simultaneously across visual and linguistic modalities. This paper introduces QBiasNet, a hybrid quantum-classical system that encodes cross-modal CLIP embeddings into an 8-qubit Variational Quantum Circuit (VQC) implemented in PennyLane. By exploiting quantum entanglement, the VQC learns high-order feature correlations that linear probes and shallow neural networks systematically miss. Evaluated on a 12,000-sample benchmark merging the FairFace and WinoBias datasets, QBiasNet achieves 91.4% bias detection accuracy and reduces the intersectional bias false negative rate by 33% relative to the strongest classical baseline. An entanglement ablation study confirms that the structural properties of the VQC are responsible for the observed performance advantage, not its parameter count. These findings suggest that quantum-enhanced classifiers are a practical and theoretically grounded tool for next-generation AI governance and regulatory compliance auditing.
