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: (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 constraine
The increasing deployment of AI, particularly SNNs, in real-world applications necessitates robust fairness evaluations to address ethical and reliability concerns before widespread adoption. This timing aligns with growing scrutiny on AI bias.
A strategic reader should care because fairness in AI is a critical prerequisite for public trust and regulatory acceptance, directly impacting the scalability and commercial viability of AI systems, especially in sensitive applications. Biased systems can lead to reputational damage, legal liabilities, and market rejection.
The introduction of a systematic benchmark for SNN fairness, considering data bias, spurious features, and hardware effects, provides a standardized method to evaluate and improve ethical AI development, potentially accelerating more responsible SNN deployment. This changes the landscape from ad-hoc assessments to structured evaluation.
- · AI ethicists
- · SNN developers focused on fairness
- · Hardware manufacturers for edge AI
- · Developers of un-audited SNNs
- · Organizations deploying biased AI
Increased focus on ethical considerations within Spiking Neural Network (SNN) development and deployment.
Development of industry standards and benchmarks for AI fairness, potentially leading to regulatory frameworks around AI ethics.
Enhanced public trust in AI systems due to demonstrable fairness, accelerating broader societal adoption in critical sectors like healthcare and finance.
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