{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:W5RVQI44DD5FAHUXF2OWLUXVRY","short_pith_number":"pith:W5RVQI44","canonical_record":{"source":{"id":"2212.08821","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2022-12-17T07:59:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"20c6ad76f717021f4ca0191cc749e19ba69c5f2db9174e38c929d9705b0bb1d3","abstract_canon_sha256":"1533bdd6501503137e84f973b3363728a3714538b6a5447616aa4a23a989276e"},"schema_version":"1.0"},"canonical_sha256":"b76358239c18fa501e972e9d65d2f58e3930aae7431cae252b7af5f66ae870f6","source":{"kind":"arxiv","id":"2212.08821","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.08821","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"arxiv_version","alias_value":"2212.08821v1","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.08821","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"pith_short_12","alias_value":"W5RVQI44DD5F","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"pith_short_16","alias_value":"W5RVQI44DD5FAHUX","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"pith_short_8","alias_value":"W5RVQI44","created_at":"2026-07-05T05:26:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:W5RVQI44DD5FAHUXF2OWLUXVRY","target":"record","payload":{"canonical_record":{"source":{"id":"2212.08821","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2022-12-17T07:59:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"20c6ad76f717021f4ca0191cc749e19ba69c5f2db9174e38c929d9705b0bb1d3","abstract_canon_sha256":"1533bdd6501503137e84f973b3363728a3714538b6a5447616aa4a23a989276e"},"schema_version":"1.0"},"canonical_sha256":"b76358239c18fa501e972e9d65d2f58e3930aae7431cae252b7af5f66ae870f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:14.696130Z","signature_b64":"yDOijW45RLArSiSZ9yKFhSEHbqTQeII5Bt4vHFG2++Siz1nF6bfx/UIPtMVW8Vm42XXUJgVCJXVtOBMIKTQ/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b76358239c18fa501e972e9d65d2f58e3930aae7431cae252b7af5f66ae870f6","last_reissued_at":"2026-07-05T05:26:14.695782Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:14.695782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.08821","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:26:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mU8CtFY61X8RBsr7DpmPZBlmUZUeoZTq9H5w174cUQ/mCvcmjkBOzoRRFFHcw+uF+sGhIHFXzYukOYUS5iuSDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:12:04.303282Z"},"content_sha256":"5b372f0c0ee9a8bc1cd6354b5610fd239f4ccfbc8ad7d83543eb0900a197ed7f","schema_version":"1.0","event_id":"sha256:5b372f0c0ee9a8bc1cd6354b5610fd239f4ccfbc8ad7d83543eb0900a197ed7f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:W5RVQI44DD5FAHUXF2OWLUXVRY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Betul Acunas, Isil Guzey, Nukhet Aladag Ciftdemir, Ozlem Ucar","submitted_at":"2022-12-17T07:59:09Z","abstract_excerpt":"Although machine learning (ML) models of AI achieve high performances in medicine, they are not free of errors. Empowering clinicians to identify incorrect model recommendations is crucial for engendering trust in medical AI. Explainable AI (XAI) aims to address this requirement by clarifying AI reasoning to support the end users. Several studies on biomedical imaging achieved promising results recently. Nevertheless, solutions for models using tabular data are not sufficient to meet the requirements of clinicians yet. This paper proposes a methodology to support clinicians in identifying fail"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.08821","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.08821/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:26:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3QntZi1hPKoe/JcXDAAqIXJojsXNX6RTzX/y6gnm5aQHhy5pMYEsTbC5x5TLHfTxxKAqGq3j+npzPwkyD2iqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:12:04.303778Z"},"content_sha256":"9e9ef1e44edbeefaec204ccaf37727acc4dc26eca8198d9b44a7f733acff9667","schema_version":"1.0","event_id":"sha256:9e9ef1e44edbeefaec204ccaf37727acc4dc26eca8198d9b44a7f733acff9667"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W5RVQI44DD5FAHUXF2OWLUXVRY/bundle.json","state_url":"https://pith.science/pith/W5RVQI44DD5FAHUXF2OWLUXVRY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W5RVQI44DD5FAHUXF2OWLUXVRY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T13:12:04Z","links":{"resolver":"https://pith.science/pith/W5RVQI44DD5FAHUXF2OWLUXVRY","bundle":"https://pith.science/pith/W5RVQI44DD5FAHUXF2OWLUXVRY/bundle.json","state":"https://pith.science/pith/W5RVQI44DD5FAHUXF2OWLUXVRY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W5RVQI44DD5FAHUXF2OWLUXVRY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:W5RVQI44DD5FAHUXF2OWLUXVRY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1533bdd6501503137e84f973b3363728a3714538b6a5447616aa4a23a989276e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2022-12-17T07:59:09Z","title_canon_sha256":"20c6ad76f717021f4ca0191cc749e19ba69c5f2db9174e38c929d9705b0bb1d3"},"schema_version":"1.0","source":{"id":"2212.08821","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.08821","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"arxiv_version","alias_value":"2212.08821v1","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.08821","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"pith_short_12","alias_value":"W5RVQI44DD5F","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"pith_short_16","alias_value":"W5RVQI44DD5FAHUX","created_at":"2026-07-05T05:26:14Z"},{"alias_kind":"pith_short_8","alias_value":"W5RVQI44","created_at":"2026-07-05T05:26:14Z"}],"graph_snapshots":[{"event_id":"sha256:9e9ef1e44edbeefaec204ccaf37727acc4dc26eca8198d9b44a7f733acff9667","target":"graph","created_at":"2026-07-05T05:26:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.08821/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although machine learning (ML) models of AI achieve high performances in medicine, they are not free of errors. Empowering clinicians to identify incorrect model recommendations is crucial for engendering trust in medical AI. Explainable AI (XAI) aims to address this requirement by clarifying AI reasoning to support the end users. Several studies on biomedical imaging achieved promising results recently. Nevertheless, solutions for models using tabular data are not sufficient to meet the requirements of clinicians yet. This paper proposes a methodology to support clinicians in identifying fail","authors_text":"Betul Acunas, Isil Guzey, Nukhet Aladag Ciftdemir, Ozlem Ucar","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2022-12-17T07:59:09Z","title":"Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.08821","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5b372f0c0ee9a8bc1cd6354b5610fd239f4ccfbc8ad7d83543eb0900a197ed7f","target":"record","created_at":"2026-07-05T05:26:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1533bdd6501503137e84f973b3363728a3714538b6a5447616aa4a23a989276e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2022-12-17T07:59:09Z","title_canon_sha256":"20c6ad76f717021f4ca0191cc749e19ba69c5f2db9174e38c929d9705b0bb1d3"},"schema_version":"1.0","source":{"id":"2212.08821","kind":"arxiv","version":1}},"canonical_sha256":"b76358239c18fa501e972e9d65d2f58e3930aae7431cae252b7af5f66ae870f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b76358239c18fa501e972e9d65d2f58e3930aae7431cae252b7af5f66ae870f6","first_computed_at":"2026-07-05T05:26:14.695782Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:26:14.695782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yDOijW45RLArSiSZ9yKFhSEHbqTQeII5Bt4vHFG2++Siz1nF6bfx/UIPtMVW8Vm42XXUJgVCJXVtOBMIKTQ/DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:26:14.696130Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.08821","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b372f0c0ee9a8bc1cd6354b5610fd239f4ccfbc8ad7d83543eb0900a197ed7f","sha256:9e9ef1e44edbeefaec204ccaf37727acc4dc26eca8198d9b44a7f733acff9667"],"state_sha256":"cb0d632306783d3824479dd711c3c1ac7e4f3930ddac8608000ae97d79acfb12"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tmhDm0NA7P28dMQVMB023fSrzRe6Lj0OSl1DhesCAmmhO8WDTV4URT8I7GRqkimvqFr0SYLInqZ4j3/hj5gMAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:12:04.308016Z","bundle_sha256":"768e09a59c35a8e2644e3b3deacb1996e0af89096da5f14aeef44855ce1987c4"}}