{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TVWCSWVS6NRE4WJVMPNKDOV53N","short_pith_number":"pith:TVWCSWVS","schema_version":"1.0","canonical_sha256":"9d6c295ab2f3624e593563daa1babddb4a351b1e676f7eafd415e2e247bf60cd","source":{"kind":"arxiv","id":"2311.07763","version":1},"attestation_state":"computed","paper":{"title":"The Disagreement Problem in Faithfulness Metrics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Brian Barr, Caleb Mok, Daniel Proano, Leif Hancox-Li, Noah Fatsi, Peter Richter","submitted_at":"2023-11-13T21:26:24Z","abstract_excerpt":"The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work. Much of the work centers around the holy grail of providing post-hoc feature attributions to any model architecture. While the pace of innovation around novel methods has slowed down, the question remains of how to choose a method, and how to make it fit for purpose. Recently, efforts around benchmarking XAI methods have suggested metrics for that purpose -- but there are many choices. That bounty of choice still leaves an end user unclear on how to proceed. This paper focuses on "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2311.07763","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-13T21:26:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"da7f36e2c38f44406f993e81b5f3e51f8c7e5239027552d94e9f106c5fc35cd2","abstract_canon_sha256":"da2981fae29263da3641ca4e19d3ae68328b0518c1d8ea75c3719ff3ab58ab2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:23.402941Z","signature_b64":"tFbha8PkeK1CVagEML3w7nR9DfQFYE6LqaUX7vDyawNe9A+AnSwKwr3K/RpLdnU+UvF0MjVBMMAGr1IQ4HDWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d6c295ab2f3624e593563daa1babddb4a351b1e676f7eafd415e2e247bf60cd","last_reissued_at":"2026-07-05T07:12:23.402421Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:23.402421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Disagreement Problem in Faithfulness Metrics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Brian Barr, Caleb Mok, Daniel Proano, Leif Hancox-Li, Noah Fatsi, Peter Richter","submitted_at":"2023-11-13T21:26:24Z","abstract_excerpt":"The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work. Much of the work centers around the holy grail of providing post-hoc feature attributions to any model architecture. While the pace of innovation around novel methods has slowed down, the question remains of how to choose a method, and how to make it fit for purpose. Recently, efforts around benchmarking XAI methods have suggested metrics for that purpose -- but there are many choices. That bounty of choice still leaves an end user unclear on how to proceed. This paper focuses on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07763","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/2311.07763/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2311.07763","created_at":"2026-07-05T07:12:23.402496+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.07763v1","created_at":"2026-07-05T07:12:23.402496+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07763","created_at":"2026-07-05T07:12:23.402496+00:00"},{"alias_kind":"pith_short_12","alias_value":"TVWCSWVS6NRE","created_at":"2026-07-05T07:12:23.402496+00:00"},{"alias_kind":"pith_short_16","alias_value":"TVWCSWVS6NRE4WJV","created_at":"2026-07-05T07:12:23.402496+00:00"},{"alias_kind":"pith_short_8","alias_value":"TVWCSWVS","created_at":"2026-07-05T07:12:23.402496+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.08502","citing_title":"Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08502","citing_title":"Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N","json":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N.json","graph_json":"https://pith.science/api/pith-number/TVWCSWVS6NRE4WJVMPNKDOV53N/graph.json","events_json":"https://pith.science/api/pith-number/TVWCSWVS6NRE4WJVMPNKDOV53N/events.json","paper":"https://pith.science/paper/TVWCSWVS"},"agent_actions":{"view_html":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N","download_json":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N.json","view_paper":"https://pith.science/paper/TVWCSWVS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.07763&json=true","fetch_graph":"https://pith.science/api/pith-number/TVWCSWVS6NRE4WJVMPNKDOV53N/graph.json","fetch_events":"https://pith.science/api/pith-number/TVWCSWVS6NRE4WJVMPNKDOV53N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N/action/storage_attestation","attest_author":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N/action/author_attestation","sign_citation":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N/action/citation_signature","submit_replication":"https://pith.science/pith/TVWCSWVS6NRE4WJVMPNKDOV53N/action/replication_record"}},"created_at":"2026-07-05T07:12:23.402496+00:00","updated_at":"2026-07-05T07:12:23.402496+00:00"}