{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UEROKB3CYUPRVIBTS6VQALKYIY","short_pith_number":"pith:UEROKB3C","canonical_record":{"source":{"id":"2408.11247","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-20T23:54:26Z","cross_cats_sorted":[],"title_canon_sha256":"b918b267ebdbee6ce2d70211eefd4be79a6cc5f3641801b4417239cf7d48bb10","abstract_canon_sha256":"2c1bca52294e174f191449cbc77ff581d52f76ad830c24b97398fbdb622286d1"},"schema_version":"1.0"},"canonical_sha256":"a122e50762c51f1aa03397ab002d584639e331f791b6bc13a78f8f3df19b7d90","source":{"kind":"arxiv","id":"2408.11247","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.11247","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"2408.11247v2","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11247","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"UEROKB3CYUPR","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"pith_short_16","alias_value":"UEROKB3CYUPRVIBT","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"pith_short_8","alias_value":"UEROKB3C","created_at":"2026-07-05T08:59:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UEROKB3CYUPRVIBTS6VQALKYIY","target":"record","payload":{"canonical_record":{"source":{"id":"2408.11247","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-20T23:54:26Z","cross_cats_sorted":[],"title_canon_sha256":"b918b267ebdbee6ce2d70211eefd4be79a6cc5f3641801b4417239cf7d48bb10","abstract_canon_sha256":"2c1bca52294e174f191449cbc77ff581d52f76ad830c24b97398fbdb622286d1"},"schema_version":"1.0"},"canonical_sha256":"a122e50762c51f1aa03397ab002d584639e331f791b6bc13a78f8f3df19b7d90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:59:32.743222Z","signature_b64":"RcEB4j9TflcM2tS1fg7vpmTScufAOKhvBGCOJRVAGfR/g0wEXNI32pBx7AYrj8dmGM1nGH4iUrvYwv0Hyhy9DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a122e50762c51f1aa03397ab002d584639e331f791b6bc13a78f8f3df19b7d90","last_reissued_at":"2026-07-05T08:59:32.742769Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:59:32.742769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.11247","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:59:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0Y9c5hlrLNDyII0L9ffphv485ai9dQk0pfq037A8Pm1cYQukZNwTq2gqpkHXhn9GLDB5C/PBFO21hNMSwr1ACA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:50:05.568186Z"},"content_sha256":"9759150478d8014ab070a82497af82030794f63353864ed15fa098f30a12b79e","schema_version":"1.0","event_id":"sha256:9759150478d8014ab070a82497af82030794f63353864ed15fa098f30a12b79e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UEROKB3CYUPRVIBTS6VQALKYIY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aman Chadha, Atmika Gorti, Manas Gaur","submitted_at":"2024-08-20T23:54:26Z","abstract_excerpt":"Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gender, occupation, and other sensitive categories. This issue becomes particularly problematic as biased LLMs can have far-reaching consequences, leading to unfair practices and exacerbating social inequalities across various domains, such as recruitment, online content moderation, or even the criminal justice system. Although prior research has focused on detecting bias in LLMs using specialized datasets designed to hi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11247","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/2408.11247/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:59:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LjGzfU+TB+10VHvvZ3K9Df++enUmcKN3DOfLtPHc5kLIKw7cRZ53Ff8D9RYNrDPnRK1xl8IE3hzcJT/0q5VJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:50:05.569175Z"},"content_sha256":"0553d7aa8320b8d7e29b2888b482d2f7a831ab6114e05bdcb036d55c9e6270be","schema_version":"1.0","event_id":"sha256:0553d7aa8320b8d7e29b2888b482d2f7a831ab6114e05bdcb036d55c9e6270be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UEROKB3CYUPRVIBTS6VQALKYIY/bundle.json","state_url":"https://pith.science/pith/UEROKB3CYUPRVIBTS6VQALKYIY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UEROKB3CYUPRVIBTS6VQALKYIY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T19:50:05Z","links":{"resolver":"https://pith.science/pith/UEROKB3CYUPRVIBTS6VQALKYIY","bundle":"https://pith.science/pith/UEROKB3CYUPRVIBTS6VQALKYIY/bundle.json","state":"https://pith.science/pith/UEROKB3CYUPRVIBTS6VQALKYIY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UEROKB3CYUPRVIBTS6VQALKYIY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UEROKB3CYUPRVIBTS6VQALKYIY","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":"2c1bca52294e174f191449cbc77ff581d52f76ad830c24b97398fbdb622286d1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-20T23:54:26Z","title_canon_sha256":"b918b267ebdbee6ce2d70211eefd4be79a6cc5f3641801b4417239cf7d48bb10"},"schema_version":"1.0","source":{"id":"2408.11247","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.11247","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"2408.11247v2","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11247","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"UEROKB3CYUPR","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"pith_short_16","alias_value":"UEROKB3CYUPRVIBT","created_at":"2026-07-05T08:59:32Z"},{"alias_kind":"pith_short_8","alias_value":"UEROKB3C","created_at":"2026-07-05T08:59:32Z"}],"graph_snapshots":[{"event_id":"sha256:0553d7aa8320b8d7e29b2888b482d2f7a831ab6114e05bdcb036d55c9e6270be","target":"graph","created_at":"2026-07-05T08:59:32Z","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/2408.11247/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gender, occupation, and other sensitive categories. This issue becomes particularly problematic as biased LLMs can have far-reaching consequences, leading to unfair practices and exacerbating social inequalities across various domains, such as recruitment, online content moderation, or even the criminal justice system. Although prior research has focused on detecting bias in LLMs using specialized datasets designed to hi","authors_text":"Aman Chadha, Atmika Gorti, Manas Gaur","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-20T23:54:26Z","title":"Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11247","kind":"arxiv","version":2},"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":"sha256:9759150478d8014ab070a82497af82030794f63353864ed15fa098f30a12b79e","target":"record","created_at":"2026-07-05T08:59:32Z","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":"2c1bca52294e174f191449cbc77ff581d52f76ad830c24b97398fbdb622286d1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-20T23:54:26Z","title_canon_sha256":"b918b267ebdbee6ce2d70211eefd4be79a6cc5f3641801b4417239cf7d48bb10"},"schema_version":"1.0","source":{"id":"2408.11247","kind":"arxiv","version":2}},"canonical_sha256":"a122e50762c51f1aa03397ab002d584639e331f791b6bc13a78f8f3df19b7d90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a122e50762c51f1aa03397ab002d584639e331f791b6bc13a78f8f3df19b7d90","first_computed_at":"2026-07-05T08:59:32.742769Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:59:32.742769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RcEB4j9TflcM2tS1fg7vpmTScufAOKhvBGCOJRVAGfR/g0wEXNI32pBx7AYrj8dmGM1nGH4iUrvYwv0Hyhy9DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:59:32.743222Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.11247","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9759150478d8014ab070a82497af82030794f63353864ed15fa098f30a12b79e","sha256:0553d7aa8320b8d7e29b2888b482d2f7a831ab6114e05bdcb036d55c9e6270be"],"state_sha256":"2d3c033b3f8d4d738f61b0080892d0f5c1f3c1eb828687268ac4e53eb689b82b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cqeUghk4WqpKwMNfSgoFxCaCjd5oJ7+U2RcfJp8d5E0GzChRuyXUaZ9uUgBeOxlmnZFCs+9nMgwUYIGnqxDeCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:50:05.575988Z","bundle_sha256":"13a1c6cc1a075f69dd240bc16ca865e87b6171ffae65b7559a641c4f4732dbac"}}