{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3BWCD45E4NX4APHFYXABWXAPZS","short_pith_number":"pith:3BWCD45E","schema_version":"1.0","canonical_sha256":"d86c21f3a4e36fc03ce5c5c01b5c0fcc8461c7515b0209391327d7a4df60b355","source":{"kind":"arxiv","id":"2604.15898","version":2},"attestation_state":"computed","paper":{"title":"Towards Rigorous Explainability by Feature Attribution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Symbolic methods can provide rigorous feature importance assignments in explainable AI unlike non-symbolic approaches such as Shapley values","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Joao Marques-Silva, Olivier L\\'etoff\\'e, Xuanxiang Huang","submitted_at":"2026-04-17T09:56:17Z","abstract_excerpt":"For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislead human decision-makers. In high-stakes uses of ML, the lack of rigor is especially problematic. One prime example of provable lack of rigor is the adoption of Shapley values in explainable artificial intelligence (XAI), with the tool SHAP being a ubiquitous example. This paper overviews the ongoing efforts towards using rigorous symbolic methods of XAI as an alternative to non-rigorous non-symbolic approaches, concr"},"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":"2604.15898","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-04-17T09:56:17Z","cross_cats_sorted":[],"title_canon_sha256":"bae305f7e70bea1fcfdcfeccfb155c0af9398a7bb31ae4509678d57dc6f0635c","abstract_canon_sha256":"d9c236ebfc2cc9dd65c47a096eae33ae38139dd7974ae95b18228d4f629857d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:05:12.286566Z","signature_b64":"tYPiMb/rWrstOgbucTrCdd0jZmDY9pQ4zyIHXqgBWaUtgBXQckevB1dcsEfSWeX17qdQBMHB1AKlepruxWHyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d86c21f3a4e36fc03ce5c5c01b5c0fcc8461c7515b0209391327d7a4df60b355","last_reissued_at":"2026-05-28T01:05:12.286051Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:05:12.286051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Rigorous Explainability by Feature Attribution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Symbolic methods can provide rigorous feature importance assignments in explainable AI unlike non-symbolic approaches such as Shapley values","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Joao Marques-Silva, Olivier L\\'etoff\\'e, Xuanxiang Huang","submitted_at":"2026-04-17T09:56:17Z","abstract_excerpt":"For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislead human decision-makers. In high-stakes uses of ML, the lack of rigor is especially problematic. One prime example of provable lack of rigor is the adoption of Shapley values in explainable artificial intelligence (XAI), with the tool SHAP being a ubiquitous example. This paper overviews the ongoing efforts towards using rigorous symbolic methods of XAI as an alternative to non-rigorous non-symbolic approaches, concr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Non-symbolic methods such as Shapley values lack rigor and can mislead, while symbolic methods provide rigorous alternatives for feature importance assignment.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That symbolic methods can be made practical and scalable for the complex, high-dimensional models used in real-world machine learning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The paper reviews work on symbolic methods for rigorous feature attribution in XAI as an alternative to non-rigorous non-symbolic techniques.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Symbolic methods can provide rigorous feature importance assignments in explainable AI unlike non-symbolic approaches such as Shapley values","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1ba0bb6a58f5018b9189c2e97f90eae142fa055cbd277f943b1f043c78e2eeef"},"source":{"id":"2604.15898","kind":"arxiv","version":2},"verdict":{"id":"73fe874b-e2b5-41b6-b2b8-c58d27341a2b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T09:12:01.066977Z","strongest_claim":"Non-symbolic methods such as Shapley values lack rigor and can mislead, while symbolic methods provide rigorous alternatives for feature importance assignment.","one_line_summary":"The paper reviews work on symbolic methods for rigorous feature attribution in XAI as an alternative to non-rigorous non-symbolic techniques.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That symbolic methods can be made practical and scalable for the complex, high-dimensional models used in real-world machine learning.","pith_extraction_headline":"Symbolic methods can provide rigorous feature importance assignments in explainable AI unlike non-symbolic approaches such as Shapley values"},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.15898/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":"2604.15898","created_at":"2026-05-28T01:05:12.286102+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.15898v2","created_at":"2026-05-28T01:05:12.286102+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.15898","created_at":"2026-05-28T01:05:12.286102+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BWCD45E4NX4","created_at":"2026-05-28T01:05:12.286102+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BWCD45E4NX4APHF","created_at":"2026-05-28T01:05:12.286102+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BWCD45E","created_at":"2026-05-28T01:05:12.286102+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS","json":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS.json","graph_json":"https://pith.science/api/pith-number/3BWCD45E4NX4APHFYXABWXAPZS/graph.json","events_json":"https://pith.science/api/pith-number/3BWCD45E4NX4APHFYXABWXAPZS/events.json","paper":"https://pith.science/paper/3BWCD45E"},"agent_actions":{"view_html":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS","download_json":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS.json","view_paper":"https://pith.science/paper/3BWCD45E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.15898&json=true","fetch_graph":"https://pith.science/api/pith-number/3BWCD45E4NX4APHFYXABWXAPZS/graph.json","fetch_events":"https://pith.science/api/pith-number/3BWCD45E4NX4APHFYXABWXAPZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS/action/storage_attestation","attest_author":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS/action/author_attestation","sign_citation":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS/action/citation_signature","submit_replication":"https://pith.science/pith/3BWCD45E4NX4APHFYXABWXAPZS/action/replication_record"}},"created_at":"2026-05-28T01:05:12.286102+00:00","updated_at":"2026-05-28T01:05:12.286102+00:00"}