{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UOTIKRXZ43M5CH4EWHMODECY4V","short_pith_number":"pith:UOTIKRXZ","schema_version":"1.0","canonical_sha256":"a3a68546f9e6d9d11f84b1d8e19058e55d11621b0e2bf5d8ed16b162bfe30dd3","source":{"kind":"arxiv","id":"2506.06330","version":1},"attestation_state":"computed","paper":{"title":"ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"James Afful","submitted_at":"2025-05-31T01:12:23Z","abstract_excerpt":"As machine learning systems are increasingly deployed in high-stakes domains such as criminal justice, finance, and healthcare, the demand for interpretable and trustworthy models has intensified. Despite the proliferation of local explanation techniques, including SHAP, LIME, and counterfactual methods, there exists no standardized, reproducible framework for their comparative evaluation, particularly in fairness-sensitive settings.\n  We introduce ExplainBench, an open-source benchmarking suite for systematic evaluation of local model explanations across ethically consequential datasets. Expl"},"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":"2506.06330","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-31T01:12:23Z","cross_cats_sorted":[],"title_canon_sha256":"b7149b1768859419e4e1ba214f22a274783984ae9e7407dd094eca7961331232","abstract_canon_sha256":"72fa97d543931ec8d866dcc80815040bff0cdc9cb0c4b499547811f6dbad66a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:48.150899Z","signature_b64":"aJJK+JmVtCN41OMwlJPBolmMcWXPrvLRACPKEvbJ/1zYC67qoRDdius/DghiGIVhRrMF0YZu8SiVHt6d9ns2Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3a68546f9e6d9d11f84b1d8e19058e55d11621b0e2bf5d8ed16b162bfe30dd3","last_reissued_at":"2026-07-05T11:17:48.150495Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:48.150495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"James Afful","submitted_at":"2025-05-31T01:12:23Z","abstract_excerpt":"As machine learning systems are increasingly deployed in high-stakes domains such as criminal justice, finance, and healthcare, the demand for interpretable and trustworthy models has intensified. Despite the proliferation of local explanation techniques, including SHAP, LIME, and counterfactual methods, there exists no standardized, reproducible framework for their comparative evaluation, particularly in fairness-sensitive settings.\n  We introduce ExplainBench, an open-source benchmarking suite for systematic evaluation of local model explanations across ethically consequential datasets. Expl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06330","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/2506.06330/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":"2506.06330","created_at":"2026-07-05T11:17:48.150553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06330v1","created_at":"2026-07-05T11:17:48.150553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06330","created_at":"2026-07-05T11:17:48.150553+00:00"},{"alias_kind":"pith_short_12","alias_value":"UOTIKRXZ43M5","created_at":"2026-07-05T11:17:48.150553+00:00"},{"alias_kind":"pith_short_16","alias_value":"UOTIKRXZ43M5CH4E","created_at":"2026-07-05T11:17:48.150553+00:00"},{"alias_kind":"pith_short_8","alias_value":"UOTIKRXZ","created_at":"2026-07-05T11:17:48.150553+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/UOTIKRXZ43M5CH4EWHMODECY4V","json":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V.json","graph_json":"https://pith.science/api/pith-number/UOTIKRXZ43M5CH4EWHMODECY4V/graph.json","events_json":"https://pith.science/api/pith-number/UOTIKRXZ43M5CH4EWHMODECY4V/events.json","paper":"https://pith.science/paper/UOTIKRXZ"},"agent_actions":{"view_html":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V","download_json":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V.json","view_paper":"https://pith.science/paper/UOTIKRXZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06330&json=true","fetch_graph":"https://pith.science/api/pith-number/UOTIKRXZ43M5CH4EWHMODECY4V/graph.json","fetch_events":"https://pith.science/api/pith-number/UOTIKRXZ43M5CH4EWHMODECY4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V/action/storage_attestation","attest_author":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V/action/author_attestation","sign_citation":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V/action/citation_signature","submit_replication":"https://pith.science/pith/UOTIKRXZ43M5CH4EWHMODECY4V/action/replication_record"}},"created_at":"2026-07-05T11:17:48.150553+00:00","updated_at":"2026-07-05T11:17:48.150553+00:00"}