{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KERZ3BCNFNIEGG6SJ3FEGNPGXE","short_pith_number":"pith:KERZ3BCN","schema_version":"1.0","canonical_sha256":"51239d844d2b50431bd24eca4335e6b900445a3f26ba25f1989b1b0e80b13787","source":{"kind":"arxiv","id":"2406.01168","version":3},"attestation_state":"computed","paper":{"title":"AI as Decision-Maker: Ethics and Risk Preferences of LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.ET","cs.HC","q-fin.EC"],"primary_cat":"econ.GN","authors_text":"Hayong Yun, Shumiao Ouyang, Xingjian Zheng","submitted_at":"2024-06-03T10:05:25Z","abstract_excerpt":"Large Language Models (LLMs) exhibit surprisingly diverse risk preferences when acting as AI decision makers, a crucial characteristic whose origins remain poorly understood despite their expanding economic roles. We analyze 50 LLMs using behavioral tasks, finding stable but diverse risk profiles. Alignment tuning for harmlessness, helpfulness, and honesty significantly increases risk aversion, causally increasing risk aversion confirmed via comparative difference analysis: a ten percent ethics increase cuts risk appetite two to eight percent. This induced caution persists against prompts and "},"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":"2406.01168","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.GN","submitted_at":"2024-06-03T10:05:25Z","cross_cats_sorted":["cs.AI","cs.CY","cs.ET","cs.HC","q-fin.EC"],"title_canon_sha256":"e84390b15b2cd1d0e95338ef5df92682cd2f6fc908e40cbd183a330f888df2e7","abstract_canon_sha256":"934026c9bb59db5e62041b90fc6ccb5f94004ce624c6d84705d6e3d0c02b97d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:58.739067Z","signature_b64":"jObf8Sq44mQG6iPB0VGwHLZ/Z+IWrZp6uYFLvZY8wk68AeUF4hisDwFY029jk8dY/DldYBJ1U7FGKG/otU+CAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51239d844d2b50431bd24eca4335e6b900445a3f26ba25f1989b1b0e80b13787","last_reissued_at":"2026-07-05T11:18:58.738527Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:58.738527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AI as Decision-Maker: Ethics and Risk Preferences of LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.ET","cs.HC","q-fin.EC"],"primary_cat":"econ.GN","authors_text":"Hayong Yun, Shumiao Ouyang, Xingjian Zheng","submitted_at":"2024-06-03T10:05:25Z","abstract_excerpt":"Large Language Models (LLMs) exhibit surprisingly diverse risk preferences when acting as AI decision makers, a crucial characteristic whose origins remain poorly understood despite their expanding economic roles. We analyze 50 LLMs using behavioral tasks, finding stable but diverse risk profiles. Alignment tuning for harmlessness, helpfulness, and honesty significantly increases risk aversion, causally increasing risk aversion confirmed via comparative difference analysis: a ten percent ethics increase cuts risk appetite two to eight percent. This induced caution persists against prompts and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01168","kind":"arxiv","version":3},"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/2406.01168/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":"2406.01168","created_at":"2026-07-05T11:18:58.738598+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01168v3","created_at":"2026-07-05T11:18:58.738598+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01168","created_at":"2026-07-05T11:18:58.738598+00:00"},{"alias_kind":"pith_short_12","alias_value":"KERZ3BCNFNIE","created_at":"2026-07-05T11:18:58.738598+00:00"},{"alias_kind":"pith_short_16","alias_value":"KERZ3BCNFNIEGG6S","created_at":"2026-07-05T11:18:58.738598+00:00"},{"alias_kind":"pith_short_8","alias_value":"KERZ3BCN","created_at":"2026-07-05T11:18:58.738598+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08556","citing_title":"Can Revealed Preferences Clarify LLM Alignment and Steering?","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE","json":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE.json","graph_json":"https://pith.science/api/pith-number/KERZ3BCNFNIEGG6SJ3FEGNPGXE/graph.json","events_json":"https://pith.science/api/pith-number/KERZ3BCNFNIEGG6SJ3FEGNPGXE/events.json","paper":"https://pith.science/paper/KERZ3BCN"},"agent_actions":{"view_html":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE","download_json":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE.json","view_paper":"https://pith.science/paper/KERZ3BCN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01168&json=true","fetch_graph":"https://pith.science/api/pith-number/KERZ3BCNFNIEGG6SJ3FEGNPGXE/graph.json","fetch_events":"https://pith.science/api/pith-number/KERZ3BCNFNIEGG6SJ3FEGNPGXE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE/action/storage_attestation","attest_author":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE/action/author_attestation","sign_citation":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE/action/citation_signature","submit_replication":"https://pith.science/pith/KERZ3BCNFNIEGG6SJ3FEGNPGXE/action/replication_record"}},"created_at":"2026-07-05T11:18:58.738598+00:00","updated_at":"2026-07-05T11:18:58.738598+00:00"}