AI RESEARCH
AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection
arXiv CS.LG
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ArXi:2603.18247v1 Announce Type: new 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.