{"as_of":"2026-08-20T17:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c38086d8ebac3e405c38fa7bc55c728693a10312e74ed267469694f5f19cebff","coverage":[{"denominator":87,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":87,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:39:37.712755Z","state":"measured"},{"denominator":88,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":88,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:27:39.859475Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T20:27:40.187069Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"cited_work":{"arxiv_id":"2501.13818","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.13818","snapshot_observed_at":"2026-08-15T20:27:40.187069Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","venue":"cs.AI","work_id":"e3073205-8d85-48c0-a0bc-0b2c435b3e5f","year":2025},"citing_paper":{"arxiv_id":"2505.13118","last_updated":"2025-05-19T13:49:05Z","snapshot_observed_at":"2026-08-20T01:31:52.550666Z","submitted_at":"2025-05-19T13:49:05Z","title":"Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T20:27:39.859475Z"},"links":{"cited_paper":"/paper/2501.13818","citing_paper":"/paper/2505.13118"},"observation_digest":"sha256:929fa675d83f646fcc60c36de5743b7b97402f0c0bf57e86448d169475f981a2","observation_id":"2161c84f-5ad2-493d-bf41-9e35d2b13579","resolution":{"observed_at":"2026-08-15T20:27:40.190361Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.13818/citation-record","integrity":"/paper/2501.13818/integrity","json":"/paper/2501.13818/citation-record.json","paper":"/paper/2501.13818"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.371501Z","title":"From attribution maps to human- understandable explanations through concept relevance propagation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.371501Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:48e4d2f5b7d3781bf2a94da0e634c0362dcdeaea82fc35c9ee5e5579b89c7acd","observation_id":"7dd8691d-a4c7-4c72-8b0c-96dfb5ff8454","resolution":{"observed_at":"2026-08-10T15:39:37.371501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.375769Z","title":"Under- standing intermediate layers using linear classi- fier probes","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.375769Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:2338f267a32941fa178a1cb304bfc68daba5dc628acbf6971660ef2773c7cee7","observation_id":"3c3e6011-9a44-40e3-9f5f-1a527c653315","resolution":{"observed_at":"2026-08-10T15:39:37.375769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.379510Z","title":"Finding and removing clever hans: Using explanation meth- ods to debug and improve deep models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.379510Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:b5bb669fdc4240f7cd27e88f5255c696bc190c478edbe486c676bb57cec4f288","observation_id":"94b16547-f112-4759-9917-1cb60e3ac57e","resolution":{"observed_at":"2026-08-10T15:39:37.379510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.383535Z","title":"On pixel-wise ex- planations for non-linear classifier decisions by layer-wise relevance propagation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.383535Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:3bdbd41b066b0ec1c9cf0e2df957517fdccee6ee984f3dd42122839d6aae3d02","observation_id":"f7b50734-df47-4c52-9857-48244f487a4c","resolution":{"observed_at":"2026-08-10T15:39:37.383535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.387339Z","title":"Reactive model correction: Mitigating harm to task-relevant features via conditional bias suppression","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.387339Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:5a1d8e3e2971564adc46f67abaabfb65a862c0d8687e490104380bb5bbe03302","observation_id":"f244cbd4-e706-4610-bd75-0af60759049c","resolution":{"observed_at":"2026-08-10T15:39:37.387339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.390953Z","title":"Understanding the role of individual units in a deep neural network","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.390953Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:59b7b3737170708109a52a037f7cb763cb6bc83fe6080e31f8ed3fe051ccd08f","observation_id":"ae32d32f-7dab-4f9d-ac9c-3d2f96bb25a7","resolution":{"observed_at":"2026-08-10T15:39:37.390953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-08-17T09:19:34.450106Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-08-10T15:39:37.394940Z","title":"Explainability for fair machine learning","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.394940Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:ab2b52a9fb6fc29286af8e981427d9012150b84c7c4495651fe027bb515e99de","observation_id":"1486d5c1-3060-4dbe-a4e0-24e4b76985f1","resolution":{"observed_at":"2026-08-10T15:39:37.394940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.398992Z","title":"Probing classifiers: Promises, shortcomings, and advances","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.398992Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:3b4b21c7b5f6953ad05e8adab372c4a3579bd7bc8cec0b2c260dfceff7f7f0d3","observation_id":"33fb3f44-42bc-4343-a222-e69b68b0dc54","resolution":{"observed_at":"2026-08-10T15:39:37.398992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.646844Z","title":"Leace: Perfect linear concept erasure in closed form","venue":null,"work_id":"67ec9f8f-a4ff-4e1d-b0b2-887f2caa0765","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.402637Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:0018da3e5d031781d83523d6e7af33d253df53d80b870d1253eaf890f55f1c5d","observation_id":"644fe819-8db5-402e-9f40-555dc19b4bb7","resolution":{"observed_at":"2026-08-10T15:39:38.650703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.636131Z","title":"Debiasing skin lesion datasets and models? not so fast","venue":null,"work_id":"7d2eb248-e3ad-4fac-9b94-31799213ee5e","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.406299Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:87a97023890298a15b50003204e2a299ad05ec57a2b71c8a8622541bbfe955dd","observation_id":"56cdf558-b861-4989-8c6d-95ea4f5efcaa","resolution":{"observed_at":"2026-08-10T15:39:38.640017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.624248Z","title":"Hyper- kvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.Sci- entific data, 7(1):283, 2020","venue":null,"work_id":"71254267-0479-486e-a380-5ff9b365de40","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.409771Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:136706006ac346bfc4039584e9c912dfc835cc965042e6f9b5cd612a9b0b60df","observation_id":"a88e6c8e-8763-4e9c-8a24-555591d01e45","resolution":{"observed_at":"2026-08-10T15:39:38.628974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.612284Z","title":"Natural images are more informative for inter- preting cnn activations than state-of-the-art syn- thetic feature visualizations","venue":null,"work_id":"8c183dc4-e4c8-4cd1-b41b-76ddad17d1d1","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.414052Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:02eb1c49c8e37f0eb7301b2a61b4f516e59a704fdf3318f941fcdd9b2c65764e","observation_id":"a3cde49e-68fc-4d6b-99e5-6f39d4083252","resolution":{"observed_at":"2026-08-10T15:39:38.616618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.600389Z","title":"Lof: identifying density-based local outliers","venue":null,"work_id":"2637e696-36c0-4342-a16c-f8c985272e18","year":2000},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.418012Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:c3fd8c84bf062e8a045a679dd9468c1a9a7ad4a72327c4ab48977fdacb8bc7a2","observation_id":"f006df30-a2b9-4e2f-9236-e104a43f976b","resolution":{"observed_at":"2026-08-10T15:39:38.604337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.588970Z","title":"Towards monosemanticity: Decomposing lan- guage models with dictionary learning","venue":null,"work_id":"3b9fd8f1-2219-4559-b002-6b86ad93b802","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.421548Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:80da074b8db5969517f9e8f90776b0857cc90d2859279edc6347721f9d1eae12","observation_id":"5673b415-1073-4820-9fb7-6aec8430394a","resolution":{"observed_at":"2026-08-10T15:39:38.592874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.576338Z","title":"Deep learn- ing outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image clas- sification task","venue":null,"work_id":"b4d7e45b-4ecf-48f4-8599-36b0bd603844","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.424901Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:630ccd368263977a3981f58bf389f3b4f6db0790d3113ce430780a808b368204","observation_id":"1653fb81-9721-4c44-8624-dbd6843a12da","resolution":{"observed_at":"2026-08-10T15:39:38.580464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.566019Z","title":"Detecting shortcut learning for fair medical ai using shortcut testing","venue":null,"work_id":"52074cb2-0dc3-417c-941a-071b343d89c7","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.428378Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:1b8ae76bdfb278aa7d7101f697f9615169969c7a490689d4365d3f2869a75794","observation_id":"89fb76ae-1f91-4d17-a394-84f0a348d3e4","resolution":{"observed_at":"2026-08-10T15:39:38.569522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.555946Z","title":"Dora: Exploring outlier representations in deep neural networks","venue":null,"work_id":"15e0a8a5-5294-4f70-b106-566d2e0b8428","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.432744Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:2f4a5a80ca418b390fb79e596a17026e34a2e956a9c0ecebb80a942a29b16f72","observation_id":"208f8805-a582-4bef-b691-11a7f16d9197","resolution":{"observed_at":"2026-08-10T15:39:38.559524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.545278Z","title":"Labeling neural representations with inverse recognition","venue":null,"work_id":"de46af61-20af-47cb-ab70-33e4c81cbbd1","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.436952Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:1a6765cce1324a7a3a86fce58a1be68e354aadd56d625956fcd8b7eb595bbe02","observation_id":"66d3ec16-bd9c-4d59-8fca-7d91d36b8cf5","resolution":{"observed_at":"2026-08-10T15:39:38.549078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.533743Z","title":"Analysis of the isic image datasets: Usage, benchmarks and recommen- dations","venue":null,"work_id":"c684cb46-7e0f-40e1-ac1a-52f497e0c7ff","year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.440682Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:15b752265dac8a479ce402c204a5770ca0ee99bdb5ba3049e14a007ebc25d293","observation_id":"505414a4-7557-4cf1-a3f4-505998f45ba0","resolution":{"observed_at":"2026-08-10T15:39:38.538255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.522281Z","title":null,"venue":null,"work_id":"877dbb9d-216d-435d-9418-7bfd1b7f4bd1","year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.445075Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:6401233135a70e79565f5c0dd2ba22488a348d21ee3b9a4564aebe6d54934a8c","observation_id":"fc9c2fd0-b5a5-46a1-8173-e168a4838f14","resolution":{"observed_at":"2026-08-10T15:39:38.526272Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.511537Z","title":"Bcn20000: Dermoscopic lesions in the wild, 2019","venue":null,"work_id":"72395b68-0247-4947-bbf2-327d9e692264","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.449664Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:bc4d9f0fe4a5f39ab6aa599bd6bf240d9e685b1f20f95776ab9498eb8c1c4451","observation_id":"d6075b2b-bbd1-4037-a0f0-50b8b32ff3ca","resolution":{"observed_at":"2026-08-10T15:39:38.515217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.499618Z","title":"Concept activation regions: A generalized frame- work for concept-based explanations","venue":null,"work_id":"3100f005-4202-4f29-96a7-c4a1181d0e3e","year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.453656Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:1fa87c38f0d671ee02672bc815c628456f3a564b254401fb7a7aba59efbb9c30","observation_id":"71eee9c5-0431-49cb-86a9-f77d4efa826c","resolution":{"observed_at":"2026-08-10T15:39:38.503915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05698","last_updated":"2026-06-09T13:29:25Z","snapshot_observed_at":"2026-08-16T13:01:41.937736Z","submitted_at":"2024-11-08T16:52:52Z","title":"Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05698","snapshot_observed_at":"2026-08-10T15:39:37.457720Z","title":"Visual-tcav: Concept-based attribution and saliency maps for post-hoc explainability in image classification","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.457720Z"},"links":{"cited_paper":"/paper/2411.05698","citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:cf0cef0421ad88fb9ff4abb2d84171c51b923ca090737dcff28eba3a8c482d20","observation_id":"f86cf2d1-2079-4fcc-a622-e85831c9bf69","resolution":{"observed_at":"2026-08-10T15:39:37.457720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.488292Z","title":"Ai for radiographic covid-19 detection se- lects shortcuts over signal","venue":null,"work_id":"6c8249d2-344d-4dba-9d85-aab61b6a0914","year":2021},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.461957Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:4fc0ed8730da682dd422df31185209eed9d393694cbb4cba447d265a092e84be","observation_id":"df754309-3a78-48dc-956f-ef785674a6cc","resolution":{"observed_at":"2026-08-10T15:39:38.491904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.477185Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"dba8e694-652e-4e89-b0d3-14b3215583c5","year":2009},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.466136Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:427d37fbf2e1cea9e2b8f4cbf7f9737f46d8e897625f3dc16d20461e9ff23e73","observation_id":"98e344b9-2a5c-4f9c-b833-b0287e283918","resolution":{"observed_at":"2026-08-10T15:39:38.480939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.466660Z","title":"Predicting parameters in deep learning","venue":null,"work_id":"8b9c8cea-18c3-4566-8ef2-442fa908e746","year":2013},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.470074Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:868caf77c03d5205550354f9d9ef5c83cf77ccf40b77a2970bf0d98b3c57a467","observation_id":"150607b9-78c1-47b7-af28-7d2c08b41486","resolution":{"observed_at":"2026-08-10T15:39:38.470219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.455914Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"3b16bf4c-8452-42ac-9da9-ce2eff1dadfe","year":2021},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.474099Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:095ed6ee922b24dc8eed35ec3d2055b35ae993084e668829e8b51caaa27ea9af","observation_id":"be988b00-94db-4f4d-b3d2-8eb059b3d7a1","resolution":{"observed_at":"2026-08-10T15:39:38.459527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.444925Z","title":"Understand- ing the (extra-) ordinary: Validating deep model decisions with prototypical concept-based expla- nations","venue":null,"work_id":"6fd7612f-983b-4506-bcd5-aad5a7b29e64","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.478415Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:acdd64ce8a8d9fb1a712da784b52f8be9a5909c557f0da154acc3e52193140e2","observation_id":"3b3531f3-bba5-44e5-b7ba-36850b5e5d6d","resolution":{"observed_at":"2026-08-10T15:39:38.448752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.433184Z","title":"From hope to safety: Unlearning bi- ases of deep models via gradient penalization in latent space","venue":null,"work_id":"48fa2297-ecc2-43ef-8926-f10336206916","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.482535Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:cd5bccf30921aa5222a10f4a720abe8b20e56c956f9ed2108b97dd82a7211908","observation_id":"f6063e5b-b7ea-4a40-a7a0-e161d3a77187","resolution":{"observed_at":"2026-08-10T15:39:38.438211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.421615Z","title":"Pure: Turning polysemantic neurons into pure features by identifying relevant cir- cuits","venue":null,"work_id":"382554dd-d0eb-46dc-9844-e29a2d667e6f","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.486438Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:542ff3deebfe6b62ab2ff59187591eb6dee799ea3412eea9224aa99adef35899","observation_id":"d60c39e8-ce4a-41bf-840b-214a1e09a591","resolution":{"observed_at":"2026-08-10T15:39:38.426206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.05398","last_updated":"2025-01-09T17:47:34Z","snapshot_observed_at":"2026-08-18T08:24:44.644119Z","submitted_at":"2025-01-09T17:47:34Z","title":"Mechanistic understanding and validation of large AI models with SemanticLens","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.05398","snapshot_observed_at":"2026-08-10T15:39:37.490562Z","title":"Mech- anistic understanding and validation of large ai models with semanticlens","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.490562Z"},"links":{"cited_paper":"/paper/2501.05398","citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:3be0349bf3067eafbdddf5516658f61696a6b491f4fca590be960695951effe5","observation_id":"7ee80454-b736-40db-9318-288842d5d04c","resolution":{"observed_at":"2026-08-10T15:39:37.490562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.10652","last_updated":"2022-09-21T20:49:26Z","snapshot_observed_at":"2026-08-16T21:36:28.067615Z","submitted_at":"2022-09-21T20:49:26Z","title":"Toy Models of Superposition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.10652","snapshot_observed_at":"2026-08-10T15:39:37.494835Z","title":"Toy models of superposition.arXiv preprint arXiv:2209.10652, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.494835Z"},"links":{"cited_paper":"/paper/2209.10652","citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:f35a2c9be9220d7cd65fce4ab6596817d136eefca8e35b056cfc79bd84df6a95","observation_id":"eec1e0d9-0215-4c87-bda4-2547cc3168a9","resolution":{"observed_at":"2026-08-10T15:39:37.494835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.411357Z","title":"Visualiz- ing higher-layer features of a deep network","venue":null,"work_id":"52996a54-c252-425d-9200-cec3c708c12a","year":2009},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.498927Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:19444f5a155eb2bd0a791f38bec302a1028ba4c0f590b87f8f48c57d9a5f63d3","observation_id":"3a733a1d-2026-47db-b228-b07cd421222d","resolution":{"observed_at":"2026-08-10T15:39:38.414859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.400505Z","title":"Craft: Con- cept recursive activation factorization for ex- plainability","venue":null,"work_id":"cb6a83b2-08e0-4e58-b313-4d0958e6ba00","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.503046Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:5048f601aee7e33f4f93e03fe04f8ebd4c60b0f8efc5663c5457ef6ae1044e35","observation_id":"cb4ab8d0-0d4d-4b21-9448-3d06116873ab","resolution":{"observed_at":"2026-08-10T15:39:38.404023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.390176Z","title":"Unlocking feature visu- alization for deep network with magnitude con- strained optimization","venue":null,"work_id":"5d647c0b-bd11-4ebf-a151-3f73d184f2db","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.506924Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:20ed77366084170fa3bc9692c44012c9b8815235b8a8a28cd42b9a9c64c2e41a","observation_id":"7a7ae970-7745-4280-bc06-00ec85bccf35","resolution":{"observed_at":"2026-08-10T15:39:38.393957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.379473Z","title":"A holis- tic approach to unifying automatic concept ex- traction and concept importance estimation","venue":null,"work_id":"6e30da74-92d4-4eac-8fd6-e93e215b37eb","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.511551Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:f8248531ad1e9427066d336669d06932848deee3a55ebb7a681b375d249e9037","observation_id":"acb678db-49fb-444d-b8a4-6276f52092c7","resolution":{"observed_at":"2026-08-10T15:39:38.383325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.368370Z","title":"The use of multiple measure- ments in taxonomic problems","venue":null,"work_id":"2c96cefe-3786-4771-8fdc-9cc655f4e34b","year":1936},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.515620Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:cb9212f00fe51b950f62ca2fdf7c35e2f546e9ee7b36ed03d5af69d710bcba4d","observation_id":"30a03ec9-40ec-4df8-b212-281dbe52aae2","resolution":{"observed_at":"2026-08-10T15:39:38.372084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.357217Z","title":"Net2vec: Quan- tifying and explaining how concepts are encoded by filters in deep neural networks","venue":null,"work_id":"9b87a655-a2e2-4e73-a391-c2fd62b0a713","year":2018},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.519173Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:72f09d9843c4a27e6800545ddcc771f012bebe8e128fe5de9343893b6cb9f426","observation_id":"79349a6d-172f-4b76-bced-0b23ed4fbbaa","resolution":{"observed_at":"2026-08-10T15:39:38.361541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.345525Z","title":"Shortcut learning in deep neural networks","venue":null,"work_id":"410cd5de-99cc-47c7-a53d-81c1d5aa7a91","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.523404Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:8f863258f7e7664f02b753bf13a9f3fbf9f3f648cecd23ee26e1b589e4bb23d4","observation_id":"8f8565b1-c9b8-42d0-82b6-b40524b3c691","resolution":{"observed_at":"2026-08-10T15:39:38.349778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.335092Z","title":"Towards automatic concept- based explanations","venue":null,"work_id":"32cc7c51-9d54-4811-a1f6-da20bf60196e","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.527138Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:5ad4b923572d1f2737a2ede95c7e7cd5a1ea542725197e492a8ae93d8034a60f","observation_id":"15c4a6be-78b1-498e-9d68-4839d1eef135","resolution":{"observed_at":"2026-08-10T15:39:38.338764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.324921Z","title":"Concept discovery and dataset exploration with singular value decomposition","venue":null,"work_id":"6b633775-f326-4b12-b164-362e58eba301","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.530616Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:e80d03b53195f66bbf4e02cada531d597a014b8538c6295beda9e9731220384a","observation_id":"830a9841-c203-43b2-a27d-a456f3df9c08","resolution":{"observed_at":"2026-08-10T15:39:38.328430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.313961Z","title":"Deep residual learning for image recog- nition","venue":null,"work_id":"75d4daf3-024d-463a-93d6-5550272ccf3d","year":2016},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.534222Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:7d1c03bf09eff2aacb780cf4c487c9c4c93bf077a351f2b57d76b57aae976d05","observation_id":"441245b8-8eac-407e-a7b1-3612e45b9d7f","resolution":{"observed_at":"2026-08-10T15:39:38.317968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.303905Z","title":"Bag of tricks for image classification with convolutional neural networks","venue":null,"work_id":"16df9d5e-c137-4612-b3bd-f07d52224950","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.537723Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:93dcf28db601c886be27b461290f82263bc6600ecfbfb05f09e3194d60cedee5","observation_id":"5669e98b-5afb-4a1a-a422-b8c851d9c7e3","resolution":{"observed_at":"2026-08-10T15:39:38.307431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.291805Z","title":"Natural language descriptions of deep visual features","venue":null,"work_id":"402c11bb-8aa3-4cd8-8bd4-b761dd1d84cd","year":2021},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.542041Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:dad6d01a66d97f0926c5481c210a979a8b9272d37d0c0571e49fbcad7f588ec5","observation_id":"b65cae46-ba3d-4fa6-88ae-020a61a38cfb","resolution":{"observed_at":"2026-08-10T15:39:38.296248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.281081Z","title":"Sparse autoencoders find highly interpretable features in language models","venue":null,"work_id":"2ffc6fe0-00fe-4a37-b39c-90623523914a","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.546423Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:00c61226dc35bb07dde881957251c6616e5d0eab09deb37f967249452e57fda8","observation_id":"b9a8a6ff-e84a-425c-b639-a7f6fdd3dcb5","resolution":{"observed_at":"2026-08-10T15:39:38.284795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.269216Z","title":"Chexpert: A large chest radio- graph dataset with uncertainty labels and expert comparison","venue":null,"work_id":"f357734e-6707-4584-933d-a0cbe875dc88","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.549954Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:2ed0d071ebf88b4d3ea57f60d6750e03c8339fb19e52093e71d53002356e7a92","observation_id":"dec37b2e-38af-4903-b105-ec4e51406818","resolution":{"observed_at":"2026-08-10T15:39:38.273289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.257615Z","title":"Interpretability beyond feature attribu- tion: Quantitative testing with concept activa- tion vectors (tcav)","venue":null,"work_id":"20229874-c260-4304-9431-74288b307913","year":2018},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.553333Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:b0348fcc497a7eb26a34ac1e7be586e2d7fdb0010acdade65d3fe3b9285062ce","observation_id":"3b949265-2c1d-4473-a850-6667ea89b3d6","resolution":{"observed_at":"2026-08-10T15:39:38.261852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.245085Z","title":"Unmasking clever hans predictors and assessing what ma- chines really learn","venue":null,"work_id":"a3b78e73-65a5-4869-8050-ce43db738643","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.556929Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:81e85ccb9efef36e1987e32c588aae18ed9f3e41b26abb5944d06c2f464ee4c8","observation_id":"2cebc29d-7468-4f8f-8526-9ae8287f2750","resolution":{"observed_at":"2026-08-10T15:39:38.249360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.232704Z","title":"Umap: Uniform man- ifold approximation and projection","venue":null,"work_id":"e853ada3-3ae2-4791-8a93-6c234f93c59c","year":2018},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.561181Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:10263f0cffcd04678f19165dbc369196a237e16fc08cc545bf86632818942c78","observation_id":"185e58ee-c376-4121-bcf7-27802dfc4c4a","resolution":{"observed_at":"2026-08-10T15:39:38.237703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.220446Z","title":"Evaluating the stability of semantic concept representations in cnns for robust explainability","venue":null,"work_id":"edd110f5-6c75-441d-9a02-1efe93fd6a7d","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.564824Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:f2d1395d089bd3ef2487f33e83dfce0548c2a9402eb650bc5d8c0d1af7799dd7","observation_id":"1755349c-420b-4245-9dac-a65c13350be0","resolution":{"observed_at":"2026-08-10T15:39:38.224860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.208735Z","title":"Visualization of neu- ral networks using saliency maps","venue":null,"work_id":"c79a4d89-34c1-41f3-bcf9-6e0d9bd82215","year":1995},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.568151Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:431b752f063b4d7f96378b65ebb5df2330f8e955280e95e3a15a4d5b83eec2bd","observation_id":"083a0adb-5366-4fc9-ae2d-1985d89ab478","resolution":{"observed_at":"2026-08-10T15:39:38.212509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.197595Z","title":"Spurious fea- tures everywhere-large-scale detection of harm- ful spurious features in imagenet","venue":null,"work_id":"e602f2cc-b4ad-4c6c-93b8-d405bb02d1c3","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.572076Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:a3bc8e4ac523c349bc0d6f8cac1f2134261165d5638d89dabbfc6b58d0ea0049","observation_id":"7a1e23f8-e523-446e-af7c-d5885828ee63","resolution":{"observed_at":"2026-08-10T15:39:38.201391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.185246Z","title":"Clip- dissect: Automatic description of neuron repre- sentations in deep vision networks","venue":null,"work_id":"413787cb-a704-4e77-9fa1-a33420cb04d3","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.575301Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:48c271f9146f5c5aea3086779c137c0473278c022ce53629deedd69843afa96d","observation_id":"c918585a-5499-4008-af53-919f521416b5","resolution":{"observed_at":"2026-08-10T15:39:38.189256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.174543Z","title":"Feature visualization","venue":null,"work_id":"38e1dca6-a0b6-449d-8b5c-8873e46aff01","year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.578858Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:1ea37408a6752162d29c4f0e4be84477670d47c7e467a2dd771ca57fb682f739","observation_id":"34755a43-03b4-446c-b314-f7b97ec96ea8","resolution":{"observed_at":"2026-08-10T15:39:38.178249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.161500Z","title":"Zoom in: An introduction to circuits","venue":null,"work_id":"f1a54147-6e9c-4107-b640-30061d5c1c12","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.582218Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:338084be9e648be4e1399c6f0d8e8d32d992fd5b9e9569205ce9c8078b30182d","observation_id":"72c9b6de-b460-4d59-b1f1-64ae66624e20","resolution":{"observed_at":"2026-08-10T15:39:38.166780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.585767Z","title":"A threshold selection method from gray-level histograms","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.585767Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:e14a3b689bc2a17a6d87338fb141100781dc08a8449214db28a4ad0af59ab139","observation_id":"110ed99b-6f00-413f-bad4-3f23916e6d63","resolution":{"observed_at":"2026-08-10T15:39:37.585767Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.142875Z","title":"Reveal to re- vise: An explainable ai life cycle for iterative bias correction of deep models","venue":null,"work_id":"fc09d57e-d892-4632-8e24-aab71cbe37c2","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.589644Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:5bac9a88bc9ffaadd564038f151b0d678899f0d835540804bc16e886d4f2d7ee","observation_id":"39aa8105-3119-46bd-ad3c-fd8e7235d939","resolution":{"observed_at":"2026-08-10T15:39:38.146555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.130630Z","title":"Navigating neural space: Revisiting concept activation vectors to over- come directional divergence","venue":null,"work_id":"376faac3-ea6a-4700-bc20-2b789f84034f","year":2025},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.593394Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:66d11e3a2e2d0567b54ffb67be79a8c636602dc1d0c347f77bf47d0ccac69e28","observation_id":"2b6ec17c-b697-46a4-a744-63e51949c802","resolution":{"observed_at":"2026-08-10T15:39:38.134907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.119061Z","title":"Py- torch: An imperative style, high-performance deep learning library","venue":null,"work_id":"1ca72aac-4513-407d-96e8-ed1fc810a7fb","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.597847Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:49975d003177fff9549e1a27446596f767e2f067cf43235c2f1e081c201034a0","observation_id":"35c8e584-c8ef-4736-bcfc-ab4da79066ae","resolution":{"observed_at":"2026-08-10T15:39:38.122810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.106909Z","title":"Interpretable data-based expla- nations for fairness debugging","venue":null,"work_id":"42472799-fe5b-4c1a-802d-7ae7ec42b8c8","year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.602764Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:b38b553f471a344f7b54f83238a1c7c3d514cf29eb6b9fc645bd3c8b2eaa963b","observation_id":"e07d5fd4-eafc-4dfd-96b0-e091a48eb28a","resolution":{"observed_at":"2026-08-10T15:39:38.110974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.01444","last_updated":"2017-04-06T09:48:20Z","snapshot_observed_at":"2026-08-14T21:08:10.420718Z","submitted_at":"2017-04-05T14:20:28Z","title":"Learning to Generate Reviews and Discovering Sentiment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.01444","snapshot_observed_at":"2026-08-10T15:39:37.606346Z","title":"Learning to generate reviews and discovering sentiment","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.606346Z"},"links":{"cited_paper":"/paper/1704.01444","citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:f4c251281ef32cd23c01625c9908db539adce389287f176f36a1a461a8865ef5","observation_id":"32f344b7-09d9-454b-8a31-1246f701f416","resolution":{"observed_at":"2026-08-10T15:39:37.606346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.093687Z","title":"Interpretations are useful: penalizing explanations to align neural networks with prior knowledge","venue":null,"work_id":"bb367d82-b992-424e-b78b-62a239fa28c1","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.611162Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:49ebeac193b4fca3d291dac6d5bcf02bc831f351d18feec893c4248a910de433","observation_id":"c6aa22e3-90d8-45d3-88a2-61c676666b44","resolution":{"observed_at":"2026-08-10T15:39:38.098189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.081938Z","title":"Right for the right reasons: training differentiable models by constraining their explanations","venue":null,"work_id":"0c007d84-03a3-46e0-b5b2-b9757b4ce50a","year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.614786Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:492b7fceee1f39654a26c3076ac75f92296499dff4a95dbdb8fe0a4c6c13338e","observation_id":"f3300fa2-c7f5-4d2e-9463-8162ef0e97b0","resolution":{"observed_at":"2026-08-10T15:39:38.085617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.070149Z","title":"Making deep neural networks right for the right scientific rea- sons by interacting with their explanations","venue":null,"work_id":"4bdae303-2a59-43a8-a670-ae239bc751a7","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.618352Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:cc01295fd942d599d0eb621b3a0ab069d59e196d1f909aed5e1c98d9e478353b","observation_id":"0c580185-4688-4bd4-83a2-6ef7303401a9","resolution":{"observed_at":"2026-08-10T15:39:38.074980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.058904Z","title":"Grad-cam: Visual explanations from deep networks via gradient- based localization","venue":null,"work_id":"62065c1e-6360-4389-ac4d-b1ad5be00678","year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.622040Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:26bfd93ae7fc14f797b5618c9d34a7150145179b813a1e6b30ab1db65205a64b","observation_id":"fb723d17-98a7-42f0-99a7-406f22b85b0e","resolution":{"observed_at":"2026-08-10T15:39:38.062752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.048023Z","title":"Very deep convolutional networks for large-scale im- age recognition","venue":null,"work_id":"9690456a-8b49-47e6-86bc-1056937a98e0","year":2015},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.626328Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:36fd226238838b0201a55161d3370641c92edbe279fdbfdfef2037a3e5475936","observation_id":"c2474dd2-5796-4a33-8fd4-6eeaa2f1d025","resolution":{"observed_at":"2026-08-10T15:39:38.052012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.035791Z","title":"Salient imagenet: How to discover spurious features in deep learning","venue":null,"work_id":"9a19a9e8-2ea9-4dd6-9db5-c57dbb5d2654","year":2022},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.630439Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:e7ab33804f28d1c72ae7f1f884138a6ca8a03f0635a46f323260fc43b30b9ca1","observation_id":"d9d7d3a4-21b2-46b2-ad86-f65c7d817288","resolution":{"observed_at":"2026-08-10T15:39:38.040180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.023245Z","title":"Explaining ma- chine learning models for clinical gait analysis","venue":null,"work_id":"6af09ce6-917a-4c6d-a6d9-65e184e905f2","year":2021},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.635054Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:ecde9999815b542a18b68c630bd46f964d86f84cb027eb362f260fbd85fcc757","observation_id":"6b5f584a-3e59-4182-9e61-ae01edca9867","resolution":{"observed_at":"2026-08-10T15:39:38.027233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:38.011252Z","title":"Deep learning for ecg analysis: Benchmarks and insights from ptb- xl","venue":null,"work_id":"7026d6c9-24a2-4119-9f5b-54dee6d4f242","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.639314Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:54c0dd4b873dd361d88a6e58c8cc3abc65d3554b6f1d75a2b17d8dae7b4f612c","observation_id":"9407f59a-33e1-43b2-bcff-c0b489bb16f0","resolution":{"observed_at":"2026-08-10T15:39:38.015817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.999517Z","title":"Intriguing prop- erties of neural networks","venue":null,"work_id":"5bee45f5-79a4-49eb-a1cf-73f74c802751","year":2014},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.644062Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:5d2bc692589b50fa4d8b85ffff23cf09277f89c0ea33f36c5e6c26e7fe2582ed","observation_id":"d5f57d7d-33e3-4843-945b-3a01f3bfa1f6","resolution":{"observed_at":"2026-08-10T15:39:38.003580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.988314Z","title":"The ham10000 dataset, a large collection of multi-source dermatoscopic images of com- mon pigmented skin lesions","venue":null,"work_id":"61030e75-6a2e-40e3-8381-07ebc07f8f67","year":2018},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.647989Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:e02ff8170c3c2bbd07f0dd915284a6408db1be3f4cb5f0c8f9a29eb173652fc9","observation_id":"87271835-a6d9-4043-a6a8-baaf8b3b3a4b","resolution":{"observed_at":"2026-08-10T15:39:37.992309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.977336Z","title":"Visualizing data using t-sne","venue":null,"work_id":"d9294d47-6ade-4705-a959-781ab713032d","year":2008},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.652752Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:2d3ecce2bd20ad1048a12a9582730491badd87ad91bdcf26bd975949c9137c1d","observation_id":"5eb92ef8-b4ef-4824-95b6-c114315ef0ae","resolution":{"observed_at":"2026-08-10T15:39:37.981146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.964779Z","title":"Multi-dimensional concept discovery (mcd): A unifying framework with completeness guarantees","venue":null,"work_id":"182e63f1-0f6b-49d1-82d8-f442a4adbe5c","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.657108Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:c2637adc6c019d493f50096b7c10a9e1a70e687985520980dda78edb34867ce5","observation_id":"bf5a2673-44e4-4789-9cba-b61dfbd5d6cd","resolution":{"observed_at":"2026-08-10T15:39:37.968769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.954040Z","title":"Ptb- xl, a large publicly available electrocardiography dataset","venue":null,"work_id":"1dbe5a82-cf49-4d78-b706-3c94e4c353c6","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.661350Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:3c431aff64d28f351e49e62f6a7f069d16de252a18cf61f9534dca2cbe9045ae","observation_id":"1546d967-2d72-4bf1-a405-d7583965cc5e","resolution":{"observed_at":"2026-08-10T15:39:37.958072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.940658Z","title":"Explaining deep learning for ecg analysis: Building blocks for auditing and knowledge discovery","venue":null,"work_id":"727734fe-2de7-45e5-b194-46ae4b7dbb93","year":2024},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.665599Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:c8f634a9feb71a19c227f1d69423de05a90baf26864f2da77605a1e7a801b009","observation_id":"8a108c6f-d902-4e66-98bd-d160eb7f3de3","resolution":{"observed_at":"2026-08-10T15:39:37.945505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.929338Z","title":"Fast diffusion-based counterfactuals for shortcut re- moval and generation","venue":null,"work_id":"6e3a39ee-909b-4dc5-8c6c-ae1978f9270f","year":2025},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.669839Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:6bbe6125a12b2ac79bd1991523784152c81e062f41a0e0f76d02a9673ddef6cc","observation_id":"8b3c8565-4d0e-41c4-a059-cda7d36cad82","resolution":{"observed_at":"2026-08-10T15:39:37.933134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.919035Z","title":"Pytorch image mod- els","venue":null,"work_id":"f8fa730e-22bd-4303-b5ba-a5762f8ceecf","year":2019},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.673795Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:ea9e2ca1e7b613a1f17777c7a81e33d6ed9dc519e89b82aac42b62d8bcb7a728","observation_id":"7c8e927a-d8fe-4306-bfe8-90fe88e6dcb3","resolution":{"observed_at":"2026-08-10T15:39:37.922681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.907991Z","title":"Discover and cure: Concept- aware mitigation of spurious correlation","venue":null,"work_id":"768917df-5989-4da0-8061-5115f47271a8","year":2023},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.677966Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:5c0198d6ac4d5f8b77efd2e3c321ba621f37b7db39cefeea1d0cd6877eef0873","observation_id":"6bae9e2a-1260-411a-8436-2bf589e51d84","resolution":{"observed_at":"2026-08-10T15:39:37.911965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.896622Z","title":"Variable generalization performance of a deep learning model to de- tect pneumonia in chest radiographs: a cross- sectional study","venue":null,"work_id":"974131cb-8dc7-45b4-b25b-54c7229d710b","year":2018},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.681696Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:3c0362885b653d45db07837e3e69d9adc0e99ba0bc79e25f8c9bb6bbbbf7b57b","observation_id":"d5685515-2862-4741-8bd3-2198d78d0144","resolution":{"observed_at":"2026-08-10T15:39:37.901306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.883942Z","title":"Invertible concept-based explanations for cnn models with non-negative concept activa- tion vectors","venue":null,"work_id":"65fa68b6-5337-4d94-905f-f261e6ef4c12","year":2021},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.685172Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:673aa1ede8b056bab341f4c8ecaac67d928a030cd45590f2edda2d865da2368e","observation_id":"c09e634a-57e6-4190-afc3-1ad8f9149c09","resolution":{"observed_at":"2026-08-10T15:39:37.888808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.871831Z","title":"right-reason","venue":null,"work_id":"2ebf6bc4-c7bc-45f8-8f1a-6209afe8d45d","year":2016},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.688857Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:3f574466db89541dbc3b40949966de10664870559f77cffe1eb566802a6ca974","observation_id":"380f1e53-ccc0-4f04-b1f7-cb5ee53ef014","resolution":{"observed_at":"2026-08-10T15:39:37.876041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.860413Z","title":"Slic superpixels compared to state- of-the-art superpixel methods","venue":null,"work_id":"690241c6-aa13-43b9-bd16-f7c0c1d7fb34","year":2012},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.694460Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:b23fe269824100d45767136cfbbe719db8bf158dc5db29a8e00ba20d34f56967","observation_id":"8895df10-f0fd-46d0-986f-0071a0725892","resolution":{"observed_at":"2026-08-10T15:39:37.864227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.849166Z","title":"Support- vector networks","venue":null,"work_id":"6526c1b1-3f43-483f-b933-c24f3ad38a57","year":1995},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.698078Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:d34c1055dd6cc3000b4af0e91e47743e64cf889d869b8636cad684e73bd44d4e","observation_id":"9ee809f4-98b7-4a47-879c-b1891820aca9","resolution":{"observed_at":"2026-08-10T15:39:37.852956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.836535Z","title":"A uni- fied approach to interpreting model predictions","venue":null,"work_id":"a62e0c17-2b1a-4dbf-a587-ebd91bc55e93","year":2017},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.701786Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:c0085c06a534c87187d7748d0ea810221721ac78c4e16fb1d5fd2b8bd3ff4eae","observation_id":"8326d7e6-d6d4-477f-8571-78c2f99dc920","resolution":{"observed_at":"2026-08-10T15:39:37.841067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.824991Z","title":"Beyond word importance: Contextual decompo- sition to extract interactions from lstms","venue":null,"work_id":"ac70cb55-62a9-4b7d-a211-f46341c6dc85","year":2018},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.705162Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:d02ac20d065a4b264130de7795b9533ef413556a72c31a530886d45dea626028","observation_id":"352af60c-2dce-4bd5-88f2-1d95a322e05a","resolution":{"observed_at":"2026-08-10T15:39:37.828873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.812780Z","title":"Null it out: Guarding protected attributes by iterative nullspace projection","venue":null,"work_id":"06cd23d1-4f96-4051-9aae-f4f4a331f68a","year":2020},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.709089Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:09f4f0f3f3c21e5d2e0896717355f172560a08d7e21c7d5eec06508099f15cdb","observation_id":"3f02e7b2-7b89-4911-b069-0a8bda0a1f80","resolution":{"observed_at":"2026-08-10T15:39:37.817340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:39:37.798667Z","title":"Editing a classifier by rewriting its prediction rules","venue":null,"work_id":"38662a86-279c-4b19-989a-5f4eff4425d1","year":2021},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.712755Z"},"links":{"citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:37bfed841cba72791fb4c06f4342b6cf1f0539a09704cef8c07ebf7d3d6dad0c","observation_id":"d100b99e-dbeb-43b5-b313-33c407701189","resolution":{"observed_at":"2026-08-10T15:39:37.804317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-18T08:25:33.377009Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data"},"reference_resolution":{"displayed":87,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":73},"total_outbound_references":87},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 1 inbound Pith citation observation for arXiv:2501.13818."}