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

Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects arXiv:2605.27407v1 Announce Type: cross Abstract: 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: