{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:I4F2SIQPSHLFEJB6V75PVVAAD6","short_pith_number":"pith:I4F2SIQP","schema_version":"1.0","canonical_sha256":"470ba9220f91d652243eaffafad4001f8cabfd8a9e3a977f93826be64304bfec","source":{"kind":"arxiv","id":"1902.01876","version":1},"attestation_state":"computed","paper":{"title":"Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Abigail Emrey, Gary Klein, Robert R. Hoffman, Shane T. Mueller, William Clancey","submitted_at":"2019-02-05T19:16:17Z","abstract_excerpt":"This is an integrative review that address the question, \"What makes for a good explanation?\" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain"},"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":"1902.01876","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-02-05T19:16:17Z","cross_cats_sorted":[],"title_canon_sha256":"f0036d8376a93ce9de1d90022e1da023bafc6e801cd3c7f6bdf051d1c1e0b6e9","abstract_canon_sha256":"4d826953ab37cae14526024f7a0af3e46b5c208be9e2a21ea9ee3d16d84be8ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:38.553196Z","signature_b64":"+oY9lFGckpyevQ7mSlmal7SeGSv/ebuvHlp4fG04hVGK8eouAYco3nifczuDCVL4Xot5tuidpf1o8l+UEAteDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"470ba9220f91d652243eaffafad4001f8cabfd8a9e3a977f93826be64304bfec","last_reissued_at":"2026-05-17T23:54:38.552590Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:38.552590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Abigail Emrey, Gary Klein, Robert R. Hoffman, Shane T. Mueller, William Clancey","submitted_at":"2019-02-05T19:16:17Z","abstract_excerpt":"This is an integrative review that address the question, \"What makes for a good explanation?\" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.01876","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":""},"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":"1902.01876","created_at":"2026-05-17T23:54:38.552675+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.01876v1","created_at":"2026-05-17T23:54:38.552675+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.01876","created_at":"2026-05-17T23:54:38.552675+00:00"},{"alias_kind":"pith_short_12","alias_value":"I4F2SIQPSHLF","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"I4F2SIQPSHLFEJB6","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"I4F2SIQP","created_at":"2026-05-18T12:33:18.533446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2407.07639","citing_title":"Explaining Graph Neural Networks for Node Similarity on Graphs","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2512.12109","citing_title":"A Neuro-Symbolic Framework for Accountability in Public-Sector AI","ref_index":60,"is_internal_anchor":true},{"citing_arxiv_id":"2602.24176","citing_title":"Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions","ref_index":48,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6","json":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6.json","graph_json":"https://pith.science/api/pith-number/I4F2SIQPSHLFEJB6V75PVVAAD6/graph.json","events_json":"https://pith.science/api/pith-number/I4F2SIQPSHLFEJB6V75PVVAAD6/events.json","paper":"https://pith.science/paper/I4F2SIQP"},"agent_actions":{"view_html":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6","download_json":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6.json","view_paper":"https://pith.science/paper/I4F2SIQP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.01876&json=true","fetch_graph":"https://pith.science/api/pith-number/I4F2SIQPSHLFEJB6V75PVVAAD6/graph.json","fetch_events":"https://pith.science/api/pith-number/I4F2SIQPSHLFEJB6V75PVVAAD6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6/action/storage_attestation","attest_author":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6/action/author_attestation","sign_citation":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6/action/citation_signature","submit_replication":"https://pith.science/pith/I4F2SIQPSHLFEJB6V75PVVAAD6/action/replication_record"}},"created_at":"2026-05-17T23:54:38.552675+00:00","updated_at":"2026-05-17T23:54:38.552675+00:00"}