Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects

Researchers introduce the first systematic fairness benchmark for Spiking Neural Networks (SNNs), revealing that biased training data causes 23% higher false positive rates for underrepresented groups, while hardware constraints amplify accuracy gaps by up to 41% in edge deployments. The study demonstrates that existing bias mitigation strategies fail under resource constraints, establishing the need for co-designed approaches that balance fairness with hardware efficiency.

This research addresses a critical gap in AI safety by examining fairness in spiking neural networks, an emerging neural computing paradigm designed for energy-efficient edge deployment. Unlike traditional deep learning benchmarks, this work systematically evaluates fairness across three realistic dimensions: demographic data imbalances, spurious feature correlations, and hardware-induced constraints. The findings reveal significant fairness degradation under real-world conditions, with biased training data producing 23% higher false positive rates for minority groups and constrained spike precision on edge devices amplifying accuracy disparities to 41%.

The research is particularly important because SNNs are gaining adoption in neuromorphic hardware platforms like Intel's Loihi 2 and SpiNNaker for autonomous systems and healthcare applications where fairness failures carry serious consequences. Previous fairness research assumed idealized computational environments, ignoring the resource constraints that define edge deployment scenarios. This work bridges that gap by testing 12 state-of-the-art SNNs against controlled bias injections and simulating realistic hardware limitations.

For the AI development community, the study highlights that bias mitigation techniques optimized for cloud-based training often fail when deployed on resource-constrained neuromorphic hardware. This creates a design tension requiring new co-optimization approaches that simultaneously address algorithmic fairness and hardware efficiency. Developers building mission-critical applications must now consider fairness-performance trade-offs explicitly during architecture selection. The public benchmark enables reproducible fairness evaluation and establishes accountability standards for neuromorphic AI systems entering healthcare and autonomous systems markets.

• →Biased training data causes 23% higher false positive rates for underrepresented groups in SNNs, while hardware constraints amplify gaps to 41% in edge deployments
• →Existing bias mitigation strategies degrade under resource constraints, requiring new co-design principles for fairness-efficient neuromorphic systems
• →First systematic fairness benchmark for SNNs integrates four cross-demographic datasets with neuromorphic hardware simulators across Loihi 2 and SpiNNaker platforms
• →Spurious feature leakage and spike precision limitations emerge as critical fairness failure modes in neuromorphic hardware deployments
• →Benchmark establishes accountability standards for SNNs in socially critical applications including healthcare and autonomous systems