AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection

• URL: http://arxiv.org/abs/2603.18247v1
• Date: Wed, 18 Mar 2026 20:12:02 GMT
• Title: AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection
• Abstract summary: AGRI-Fidelity is a reliability-oriented evaluation framework for listenable explanations in poultry disease detection without spatial ground truth.<n>Across real and controlled datasets, AGRI-Fidelity effectively provides reliability-aware discrimination for all data points versus masking-based metrics.
• Score: 4.2983704620741685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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