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

Evaluating fairness in Spiking Neural Networks (SNNs) demands rigorous benchmarks that reflect real-world complexities, yet existing assessments remain limited by superficial dataset diversity. This work introduces the first systematic fairness benchmark for SNNs, addressing two critical dimensions of realism: ($1$) demographic coverage gaps in training data and ($2$) spurious feature leakage (e.g., skin tone as a proxy for class labels). Our framework integrates four cross-demographic datasets together with a saturation-based cleaning protocol that yields paired original and cleaned variants, enabling controlled comparison across data conditions. Standardized evaluations of state-of-the-art SNNs $12$ reveal pervasive fairness deficiencies, including stark disparities between demographic groups and systematic shortcut learning driven by spurious low-level cues. Our code is available at: https://anonymous.4open.science/r/SNN-Benchmarks-8017.