{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6BUFBMW5QK4OB7S6ISM3WEIYTE","short_pith_number":"pith:6BUFBMW5","schema_version":"1.0","canonical_sha256":"f06850b2dd82b8e0fe5e4499bb1118993afd173f4fc2da99f79d6f2ec24a38fa","source":{"kind":"arxiv","id":"2507.06029","version":2},"attestation_state":"computed","paper":{"title":"Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Courtney Ford, Mark T. Keane","submitted_at":"2025-07-08T14:32:25Z","abstract_excerpt":"Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a post hoc method that enhances interpretability by selecting class-representative examples using both local and global feature importance. In a user study (N = 98) evaluating Kannada script classifications, FGNS significantly improved non-experts' ability to identify model errors while maintaining appropriate agreement with correct predictions. Participants made faster and more accurate decisions compared to those given"},"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":"2507.06029","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-08T14:32:25Z","cross_cats_sorted":[],"title_canon_sha256":"2fc93a7f665f5de96cb1e8d9750f077096e9e94458195ff8e9b16514354e61fd","abstract_canon_sha256":"26eb97ad8783289f0f53e7beb23dd9e32e3802dacc697cc3c8733deb063ce377"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:17.307952Z","signature_b64":"MRGXt1zRtSBF7lPXtQQknrdNynwmMBxaBGdLVuEmFQAhP/eR4mUu1Sw5u/fTL/Ynf8buAffKsrX/QwL6zdTjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f06850b2dd82b8e0fe5e4499bb1118993afd173f4fc2da99f79d6f2ec24a38fa","last_reissued_at":"2026-07-05T11:59:17.307382Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:17.307382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Courtney Ford, Mark T. Keane","submitted_at":"2025-07-08T14:32:25Z","abstract_excerpt":"Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a post hoc method that enhances interpretability by selecting class-representative examples using both local and global feature importance. In a user study (N = 98) evaluating Kannada script classifications, FGNS significantly improved non-experts' ability to identify model errors while maintaining appropriate agreement with correct predictions. Participants made faster and more accurate decisions compared to those given"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06029","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/2507.06029/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":"2507.06029","created_at":"2026-07-05T11:59:17.307466+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.06029v2","created_at":"2026-07-05T11:59:17.307466+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06029","created_at":"2026-07-05T11:59:17.307466+00:00"},{"alias_kind":"pith_short_12","alias_value":"6BUFBMW5QK4O","created_at":"2026-07-05T11:59:17.307466+00:00"},{"alias_kind":"pith_short_16","alias_value":"6BUFBMW5QK4OB7S6","created_at":"2026-07-05T11:59:17.307466+00:00"},{"alias_kind":"pith_short_8","alias_value":"6BUFBMW5","created_at":"2026-07-05T11:59:17.307466+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/6BUFBMW5QK4OB7S6ISM3WEIYTE","json":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE.json","graph_json":"https://pith.science/api/pith-number/6BUFBMW5QK4OB7S6ISM3WEIYTE/graph.json","events_json":"https://pith.science/api/pith-number/6BUFBMW5QK4OB7S6ISM3WEIYTE/events.json","paper":"https://pith.science/paper/6BUFBMW5"},"agent_actions":{"view_html":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE","download_json":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE.json","view_paper":"https://pith.science/paper/6BUFBMW5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.06029&json=true","fetch_graph":"https://pith.science/api/pith-number/6BUFBMW5QK4OB7S6ISM3WEIYTE/graph.json","fetch_events":"https://pith.science/api/pith-number/6BUFBMW5QK4OB7S6ISM3WEIYTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE/action/storage_attestation","attest_author":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE/action/author_attestation","sign_citation":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE/action/citation_signature","submit_replication":"https://pith.science/pith/6BUFBMW5QK4OB7S6ISM3WEIYTE/action/replication_record"}},"created_at":"2026-07-05T11:59:17.307466+00:00","updated_at":"2026-07-05T11:59:17.307466+00:00"}