提出新评估框架，让禽病听觉解释更可靠
AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection
- 用多模型共识+时序循环置换构造零假设分布
- 在真实与模拟数据上显著降低虚假可靠解释
- 适合关注音频解释可信度的研究者
现有XAI度量方法仅针对单一模型，忽视了近优分类器可能依赖不同甚至虚假声学线索的多重性。在嘈杂养殖场环境中，通风噪声等固定干扰物会生成看似忠实却不可靠的解释，因基于掩码的度量无法惩罚冗余捷径。本文提出AGRI-Fidelity，一种无需空间真值即可评估禽病检测可听解释可靠性的框架。该方法结合跨模型一致性与循环时间置换，构建零分布并计算假发现率（FDR），有效抑制固定干扰物影响，同时保留时间局部化的生物声学特征。在真实与受控数据集上，该方法均能对所有数据点实现可靠性感知的区分，优于基于掩码的度量。
原文摘要 · Abstract (English)
Existing XAI metrics measure faithfulness for a single model, ignoring model multiplicity where near-optimal classifiers rely on different or spurious acoustic cues. In noisy farm environments, stationary artifacts such as ventilation noise can produce explanations that are faithful yet unreliable, as masking-based metrics fail to penalize redundant shortcuts. We propose AGRI-Fidelity, a reliability-oriented evaluation framework for listenable explanations in poultry disease detection without spatial ground truth. The method combines cross-model consensus with cyclic temporal permutation to construct null distributions and compute a False Discovery Rate (FDR), suppressing stationary artifacts while preserving time-localized bioacoustic markers. Across real and controlled datasets, AGRI-Fidelity effectively provides reliability-aware discrimination for all data points versus masking-based metrics.
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