{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QBGOEAP4FR7KPSNHML4IIKBTFF","short_pith_number":"pith:QBGOEAP4","schema_version":"1.0","canonical_sha256":"804ce201fc2c7ea7c9a762f8842833294abd73b4779b07702c13043478364827","source":{"kind":"arxiv","id":"2112.01016","version":1},"attestation_state":"computed","paper":{"title":"On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.HC","authors_text":"Erwen Senge, Helen Jiang","submitted_at":"2021-12-02T07:02:27Z","abstract_excerpt":"Explainable AI (XAI) research has been booming, but the question \"$\\textbf{To whom}$ are we making AI explainable?\" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered \"explained\" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments.\n  We advocate that it is critical to develop XAI "},"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":"2112.01016","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.HC","submitted_at":"2021-12-02T07:02:27Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"6273ef46446050f419cb2183914de9081d6d71a21a1f44deb29b58a1bfd22b0e","abstract_canon_sha256":"2463a691234a1eaedfa3188b61e0820e53fc7ffe52096275211ca1e4ba935d36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:37:02.638257Z","signature_b64":"nTk3/HiwZ2IUYb9UHNiZcGUrueQLLsgvGoixyY7o6m3qySb9JiP54d+NH6qrRnXt5PyJZE6yNB9yDeYBmVKwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"804ce201fc2c7ea7c9a762f8842833294abd73b4779b07702c13043478364827","last_reissued_at":"2026-07-05T03:37:02.637757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:37:02.637757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.HC","authors_text":"Erwen Senge, Helen Jiang","submitted_at":"2021-12-02T07:02:27Z","abstract_excerpt":"Explainable AI (XAI) research has been booming, but the question \"$\\textbf{To whom}$ are we making AI explainable?\" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered \"explained\" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments.\n  We advocate that it is critical to develop XAI "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.01016","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/2112.01016/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":"2112.01016","created_at":"2026-07-05T03:37:02.637815+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.01016v1","created_at":"2026-07-05T03:37:02.637815+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.01016","created_at":"2026-07-05T03:37:02.637815+00:00"},{"alias_kind":"pith_short_12","alias_value":"QBGOEAP4FR7K","created_at":"2026-07-05T03:37:02.637815+00:00"},{"alias_kind":"pith_short_16","alias_value":"QBGOEAP4FR7KPSNH","created_at":"2026-07-05T03:37:02.637815+00:00"},{"alias_kind":"pith_short_8","alias_value":"QBGOEAP4","created_at":"2026-07-05T03:37:02.637815+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.05145","citing_title":"Explingo: Explaining AI Predictions using Large Language Models","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF","json":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF.json","graph_json":"https://pith.science/api/pith-number/QBGOEAP4FR7KPSNHML4IIKBTFF/graph.json","events_json":"https://pith.science/api/pith-number/QBGOEAP4FR7KPSNHML4IIKBTFF/events.json","paper":"https://pith.science/paper/QBGOEAP4"},"agent_actions":{"view_html":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF","download_json":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF.json","view_paper":"https://pith.science/paper/QBGOEAP4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.01016&json=true","fetch_graph":"https://pith.science/api/pith-number/QBGOEAP4FR7KPSNHML4IIKBTFF/graph.json","fetch_events":"https://pith.science/api/pith-number/QBGOEAP4FR7KPSNHML4IIKBTFF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF/action/storage_attestation","attest_author":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF/action/author_attestation","sign_citation":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF/action/citation_signature","submit_replication":"https://pith.science/pith/QBGOEAP4FR7KPSNHML4IIKBTFF/action/replication_record"}},"created_at":"2026-07-05T03:37:02.637815+00:00","updated_at":"2026-07-05T03:37:02.637815+00:00"}