{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3M5IVRSLCHZ4OTPHWLR5X3OFND","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"446dfce20276aaf898fe4e5e0305b20c0390de5eb5ffa3fd64d0529b25291b5e","cross_cats_sorted":["q-fin.EC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2024-03-22T15:23:19Z","title_canon_sha256":"0b77aa602e728e7491592f0f5b54297ffd06c9f0b4cca3fb5add1887bf2b5e03"},"schema_version":"1.0","source":{"id":"2403.15281","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.15281","created_at":"2026-07-05T07:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"2403.15281v1","created_at":"2026-07-05T07:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15281","created_at":"2026-07-05T07:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"3M5IVRSLCHZ4","created_at":"2026-07-05T07:59:32Z"},{"alias_kind":"pith_short_16","alias_value":"3M5IVRSLCHZ4OTPH","created_at":"2026-07-05T07:59:32Z"},{"alias_kind":"pith_short_8","alias_value":"3M5IVRSL","created_at":"2026-07-05T07:59:32Z"}],"graph_snapshots":[{"event_id":"sha256:06b2f63ec2f0af3f0b2ae3812ec3666b9930a06601347fd6d56f9398fff96a38","target":"graph","created_at":"2026-07-05T07:59:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.15281/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of Large language model based artificial intelligence suggests a potential transition from human to AI based decision making. How would this impact the distributional outcomes across social groups? Here we investigate the gender and racial biases of OpenAIs GPT, a widely used LLM, in a high stakes decision making setting, specifically assessing entry level job ca","authors_text":"Chen Lin, Difang Huang, Jiafu An, Mingzhu Tai","cross_cats":["q-fin.EC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2024-03-22T15:23:19Z","title":"Measuring Gender and Racial Biases in Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15281","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2a02a00acad1552c564203cc4fbf6bbb681bd9038d7b258df659d0b9e6154668","target":"record","created_at":"2026-07-05T07:59:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"446dfce20276aaf898fe4e5e0305b20c0390de5eb5ffa3fd64d0529b25291b5e","cross_cats_sorted":["q-fin.EC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2024-03-22T15:23:19Z","title_canon_sha256":"0b77aa602e728e7491592f0f5b54297ffd06c9f0b4cca3fb5add1887bf2b5e03"},"schema_version":"1.0","source":{"id":"2403.15281","kind":"arxiv","version":1}},"canonical_sha256":"db3a8ac64b11f3c74de7b2e3dbedc568fbf133183c4569cb95a5881e5a06cdc7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db3a8ac64b11f3c74de7b2e3dbedc568fbf133183c4569cb95a5881e5a06cdc7","first_computed_at":"2026-07-05T07:59:32.720578Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:59:32.720578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BpbqL7GcNnctRrP4MLq+T81Yg8fmSSnJDPwAx5k2BTvNiAcHkNZ/PBXNP/eeTbFjnR2wzzZgZSRUOLaG1m3hBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:59:32.720978Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.15281","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a02a00acad1552c564203cc4fbf6bbb681bd9038d7b258df659d0b9e6154668","sha256:06b2f63ec2f0af3f0b2ae3812ec3666b9930a06601347fd6d56f9398fff96a38"],"state_sha256":"f42888c7f7e7600d4682c54203384c1ba16d769cba28b44d5a732a3a6c3a5cd1"}