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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.18247 |
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| _version_ | 1866914408575270912 |
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| author | Madabushi, Sindhuja Dogan, Arda Liu, Jonathan Chen, Dian Ha, Dong S. Shin, Sook Noh, Sam H. Cho, Jin-Hee |
| author_facet | Madabushi, Sindhuja Dogan, Arda Liu, Jonathan Chen, Dian Ha, Dong S. Shin, Sook Noh, Sam H. Cho, Jin-Hee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection Madabushi, Sindhuja Dogan, Arda Liu, Jonathan Chen, Dian Ha, Dong S. Shin, Sook Noh, Sam H. Cho, Jin-Hee Machine Learning 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. |
| title | AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.18247 |