{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SWPN2HHKPDR2YHTZRHWXALADAV","short_pith_number":"pith:SWPN2HHK","schema_version":"1.0","canonical_sha256":"959edd1cea78e3ac1e7989ed702c03057da4b535d224db65e191e9038a1ec3ec","source":{"kind":"arxiv","id":"2308.04635","version":2},"attestation_state":"computed","paper":{"title":"Where's the Liability in Harmful AI Speech?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Mark Lemley, Peter Henderson, Tatsunori Hashimoto","submitted_at":"2023-08-09T00:13:00Z","abstract_excerpt":"Generative AI, in particular text-based \"foundation models\" (large models trained on a huge variety of information including the internet), can generate speech that could be problematic under a wide range of liability regimes. Machine learning practitioners regularly \"red team\" models to identify and mitigate such problematic speech: from \"hallucinations\" falsely accusing people of serious misconduct to recipes for constructing an atomic bomb. A key question is whether these red-teamed behaviors actually present any liability risk for model creators and deployers under U.S. law, incentivizing "},"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":"2308.04635","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2023-08-09T00:13:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b2b99e95373b1765669aa42868bb92e2994d80f1d0fd3a5e183b0d3a0a964b22","abstract_canon_sha256":"a053bb5e40c03d9938bed06f2fb42ab0007f9f2fa0d1fac43cc25ef18f5f53a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:07.305787Z","signature_b64":"fVnPYQAWZ2xCyOhmTwhTkUoqRzp/ASHp2C8ulHg7Np9D3pSzKfbftaGa8mGvYQQaNCV/A40kVvdzBFyNXVVTDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"959edd1cea78e3ac1e7989ed702c03057da4b535d224db65e191e9038a1ec3ec","last_reissued_at":"2026-07-05T06:42:07.305448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:07.305448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Where's the Liability in Harmful AI Speech?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Mark Lemley, Peter Henderson, Tatsunori Hashimoto","submitted_at":"2023-08-09T00:13:00Z","abstract_excerpt":"Generative AI, in particular text-based \"foundation models\" (large models trained on a huge variety of information including the internet), can generate speech that could be problematic under a wide range of liability regimes. Machine learning practitioners regularly \"red team\" models to identify and mitigate such problematic speech: from \"hallucinations\" falsely accusing people of serious misconduct to recipes for constructing an atomic bomb. A key question is whether these red-teamed behaviors actually present any liability risk for model creators and deployers under U.S. law, incentivizing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.04635","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/2308.04635/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":"2308.04635","created_at":"2026-07-05T06:42:07.305502+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.04635v2","created_at":"2026-07-05T06:42:07.305502+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.04635","created_at":"2026-07-05T06:42:07.305502+00:00"},{"alias_kind":"pith_short_12","alias_value":"SWPN2HHKPDR2","created_at":"2026-07-05T06:42:07.305502+00:00"},{"alias_kind":"pith_short_16","alias_value":"SWPN2HHKPDR2YHTZ","created_at":"2026-07-05T06:42:07.305502+00:00"},{"alias_kind":"pith_short_8","alias_value":"SWPN2HHK","created_at":"2026-07-05T06:42:07.305502+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/SWPN2HHKPDR2YHTZRHWXALADAV","json":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV.json","graph_json":"https://pith.science/api/pith-number/SWPN2HHKPDR2YHTZRHWXALADAV/graph.json","events_json":"https://pith.science/api/pith-number/SWPN2HHKPDR2YHTZRHWXALADAV/events.json","paper":"https://pith.science/paper/SWPN2HHK"},"agent_actions":{"view_html":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV","download_json":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV.json","view_paper":"https://pith.science/paper/SWPN2HHK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.04635&json=true","fetch_graph":"https://pith.science/api/pith-number/SWPN2HHKPDR2YHTZRHWXALADAV/graph.json","fetch_events":"https://pith.science/api/pith-number/SWPN2HHKPDR2YHTZRHWXALADAV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV/action/storage_attestation","attest_author":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV/action/author_attestation","sign_citation":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV/action/citation_signature","submit_replication":"https://pith.science/pith/SWPN2HHKPDR2YHTZRHWXALADAV/action/replication_record"}},"created_at":"2026-07-05T06:42:07.305502+00:00","updated_at":"2026-07-05T06:42:07.305502+00:00"}