{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:FBDWL37D7F4R7IJVQVTECTJTWY","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":"22d527cc2de96d0c00f461a7a91ccd4407b23cdadda8cd64d04143dc989d8f39","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-05-26T15:52:08Z","title_canon_sha256":"1f83d8a2b03f3b2ed49544d7e1adf75d4a33c67c0a598bff1c507ce33e52fb5a"},"schema_version":"1.0","source":{"id":"2305.17043","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17043","created_at":"2026-07-05T08:38:52Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17043v2","created_at":"2026-07-05T08:38:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17043","created_at":"2026-07-05T08:38:52Z"},{"alias_kind":"pith_short_12","alias_value":"FBDWL37D7F4R","created_at":"2026-07-05T08:38:52Z"},{"alias_kind":"pith_short_16","alias_value":"FBDWL37D7F4R7IJV","created_at":"2026-07-05T08:38:52Z"},{"alias_kind":"pith_short_8","alias_value":"FBDWL37D","created_at":"2026-07-05T08:38:52Z"}],"graph_snapshots":[{"event_id":"sha256:89f1beb37939569be5bb8d01a2a8c81de30442c9b286abda20cfd4dbae3b8782","target":"graph","created_at":"2026-07-05T08:38:52Z","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/2305.17043/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the local (attributions per sample) and global (based on domain expert concepts) perspectives. We have established a set of sanity checks to identify sensible attributio","authors_text":"Nils Strodthoff, Patrick Wagner, Temesgen Mehari, Wilhelm Haverkamp","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-05-26T15:52:08Z","title":"Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17043","kind":"arxiv","version":2},"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":"sha256:3380521d739ab1cb9fd5c55081dfbed4803818ecfaf0f70a5f1c4490387d236d","target":"record","created_at":"2026-07-05T08:38:52Z","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":"22d527cc2de96d0c00f461a7a91ccd4407b23cdadda8cd64d04143dc989d8f39","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-05-26T15:52:08Z","title_canon_sha256":"1f83d8a2b03f3b2ed49544d7e1adf75d4a33c67c0a598bff1c507ce33e52fb5a"},"schema_version":"1.0","source":{"id":"2305.17043","kind":"arxiv","version":2}},"canonical_sha256":"284765efe3f9791fa1358566414d33b60e6009fe5a74558098a9f6640a5bed86","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"284765efe3f9791fa1358566414d33b60e6009fe5a74558098a9f6640a5bed86","first_computed_at":"2026-07-05T08:38:52.000573Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:52.000573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CcB2u0tqApDfRwdo2Tgvv8Jrd8x3Ue9iZ+uze5beZUhgZMMMJouPPp2oOBxmPdLKTKEOkO6x+DmrXvlyocUDCw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:52.001060Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.17043","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3380521d739ab1cb9fd5c55081dfbed4803818ecfaf0f70a5f1c4490387d236d","sha256:89f1beb37939569be5bb8d01a2a8c81de30442c9b286abda20cfd4dbae3b8782"],"state_sha256":"a80d6518bc6be1c388fe32d44bab485bb244bc943a230d8bf4575b00e7a6f6f5"}