AINeutralarXiv – CS AI · 3h ago7/10
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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.