{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GV6DFYHYYPBVOY6UTNVLGMNUK4","short_pith_number":"pith:GV6DFYHY","schema_version":"1.0","canonical_sha256":"357c32e0f8c3c35763d49b6ab331b4573c014dced555d270bf8465c42670a565","source":{"kind":"arxiv","id":"2207.14160","version":2},"attestation_state":"computed","paper":{"title":"Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Eyke H\\\"ullermeier, Maximilian Rabus, Mohamed Karim Belaid, Ralf Krestel","submitted_at":"2022-06-08T06:13:39Z","abstract_excerpt":"In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy metric by, e.g., debugging the learned pattern and demystifying the AI's behavior. The widespread use of xAI brought new challenges. On the one hand, the number of published xAI algorithms underwent a boom, and it became difficult for practitioners to select the right tool. On the other hand, some experiments did highlight how easy data scientists could misuse xAI algorithms and misinterpret their results. To tackle "},"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":"2207.14160","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SE","submitted_at":"2022-06-08T06:13:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2f7067f866befe9f5dbe19d7a3be9d373af14452d05e01eec5ad1e4c8f9a6be9","abstract_canon_sha256":"569634d5d783ce69549a84d344b1129eddf8f9a31f9f7b6b59bb2b30d357d79c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:03:04.693367Z","signature_b64":"O45tFJB9UFaIxva6h36Fxjtu9O0FGrmUF+DXRTAQmqbeTAgbTcs1tmg0GU9ZZsDQC5eVwapgGJ5vmfi+dRjfDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"357c32e0f8c3c35763d49b6ab331b4573c014dced555d270bf8465c42670a565","last_reissued_at":"2026-07-05T05:03:04.692918Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:03:04.692918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Eyke H\\\"ullermeier, Maximilian Rabus, Mohamed Karim Belaid, Ralf Krestel","submitted_at":"2022-06-08T06:13:39Z","abstract_excerpt":"In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy metric by, e.g., debugging the learned pattern and demystifying the AI's behavior. The widespread use of xAI brought new challenges. On the one hand, the number of published xAI algorithms underwent a boom, and it became difficult for practitioners to select the right tool. On the other hand, some experiments did highlight how easy data scientists could misuse xAI algorithms and misinterpret their results. To tackle "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.14160","kind":"arxiv","version":2},"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/2207.14160/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":"2207.14160","created_at":"2026-07-05T05:03:04.692982+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.14160v2","created_at":"2026-07-05T05:03:04.692982+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.14160","created_at":"2026-07-05T05:03:04.692982+00:00"},{"alias_kind":"pith_short_12","alias_value":"GV6DFYHYYPBV","created_at":"2026-07-05T05:03:04.692982+00:00"},{"alias_kind":"pith_short_16","alias_value":"GV6DFYHYYPBVOY6U","created_at":"2026-07-05T05:03:04.692982+00:00"},{"alias_kind":"pith_short_8","alias_value":"GV6DFYHY","created_at":"2026-07-05T05:03:04.692982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.06253","citing_title":"The State of Post-Hoc Local XAI Techniques for Image Processing: Challenges and Motivations","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4","json":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4.json","graph_json":"https://pith.science/api/pith-number/GV6DFYHYYPBVOY6UTNVLGMNUK4/graph.json","events_json":"https://pith.science/api/pith-number/GV6DFYHYYPBVOY6UTNVLGMNUK4/events.json","paper":"https://pith.science/paper/GV6DFYHY"},"agent_actions":{"view_html":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4","download_json":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4.json","view_paper":"https://pith.science/paper/GV6DFYHY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.14160&json=true","fetch_graph":"https://pith.science/api/pith-number/GV6DFYHYYPBVOY6UTNVLGMNUK4/graph.json","fetch_events":"https://pith.science/api/pith-number/GV6DFYHYYPBVOY6UTNVLGMNUK4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4/action/storage_attestation","attest_author":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4/action/author_attestation","sign_citation":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4/action/citation_signature","submit_replication":"https://pith.science/pith/GV6DFYHYYPBVOY6UTNVLGMNUK4/action/replication_record"}},"created_at":"2026-07-05T05:03:04.692982+00:00","updated_at":"2026-07-05T05:03:04.692982+00:00"}