Evaluating fairness in Spiking Neural Networks (SNNs) demands rigorous benchmarks that reflect real-world complexities, yet existing assessments remain limited by superficial dataset diversity and idealized hardware assumptions. This work introduces the first systematic fairness benchmark for SNNs, addressing three critical dimensions of realism: (1) demographic coverage gaps in training data, (2) spurious feature leakage (e.g., skin tone as a proxy for class labels), and (3) deployment-environment mismatches (e.g., edge devices with constrained spike encoding). Our framework integrates four cross-demographic datasets with controlled bias injections and three neuromorphic hardware simulators (Loihi 2, SpiNNaker), enabling isolated analysis of fairness-performance trade-offs under resource constraints. Standardized evaluations of 12 state-of-the-art SNNs reveal stark disparities: models trained on biased data exhibit 23\% higher false positive rates for underrepresented groups, while hardware limitations (e.g., reduced spike precision) further amplify accuracy gaps by up to 41\% in edge deployments. Critically, bias mitigation strategies developed for cloud-based SNNs often degrade under resource constraints, highlighting the need for co-design principles that jointly optimize fairness and hardware efficiency. By bridging algorithmic fairness research with neuromorphic engineering, our benchmark provides a foundation for trustworthy SNNs in socially critical applications such as healthcare and autonomous systems. Our code is available at: https://anonymous.4open.science/r/SNN-Benchmarks-8017.

翻译：评估脉冲神经网络（SNNs）的公平性需要能够反映现实复杂性的严格基准，然而现有评估仍受限于肤浅的数据集多样性和理想化的硬件假设。本文首次提出针对SNNs的系统性公平性基准测试，涵盖三个关键现实维度：（1）训练数据中的人口覆盖缺口，（2）虚假特征泄漏（例如肤色作为类别标签的代理变量），以及（3）部署环境不匹配（例如受限脉冲编码的边缘设备）。我们的框架整合了四个跨人口数据集（含受控偏差注入）与三种神经形态硬件模拟器（Loihi 2、SpiNNaker），能够在资源约束下独立分析公平性与性能的权衡关系。对12种最先进SNNs的标准化评估揭示了显著差异：在偏差数据上训练的模型对弱势群体的假阳性率高出23%，而硬件限制（如降低的脉冲精度）进一步将边缘部署中的准确率差距放大至41%。更为关键的是，为云端SNNs开发的偏差缓解策略在资源约束下往往性能退化，凸显了联合优化公平性与硬件效率的协同设计原则的必要性。通过连接算法公平性研究与神经形态工程，我们的基准测试为社会关键应用（如医疗保健和自主系统）中的可信SNNs奠定了基础。代码开源地址：https://anonymous.4open.science/r/SNN-Benchmarks-8017。