{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:7H6VEDOPTLBUG373BGBXLUJL2J","short_pith_number":"pith:7H6VEDOP","canonical_record":{"source":{"id":"2506.17490","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2025-06-20T21:58:00Z","cross_cats_sorted":["q-fin.EC"],"title_canon_sha256":"8de011ab6cf2bcd8d925b795d329bf2a2a17a5fb7162da552a3a82a512320cdc","abstract_canon_sha256":"e0659905f2032ca344c3778d0f5f5551c89aa3dd01209b9a0fd126705ca3461f"},"schema_version":"1.0"},"canonical_sha256":"f9fd520dcf9ac3436ffb098375d12bd27b34a0e46c43f22fa211601d1a9b2515","source":{"kind":"arxiv","id":"2506.17490","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.17490","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"arxiv_version","alias_value":"2506.17490v1","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17490","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"pith_short_12","alias_value":"7H6VEDOPTLBU","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"pith_short_16","alias_value":"7H6VEDOPTLBUG373","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"pith_short_8","alias_value":"7H6VEDOP","created_at":"2026-07-05T11:25:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:7H6VEDOPTLBUG373BGBXLUJL2J","target":"record","payload":{"canonical_record":{"source":{"id":"2506.17490","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2025-06-20T21:58:00Z","cross_cats_sorted":["q-fin.EC"],"title_canon_sha256":"8de011ab6cf2bcd8d925b795d329bf2a2a17a5fb7162da552a3a82a512320cdc","abstract_canon_sha256":"e0659905f2032ca344c3778d0f5f5551c89aa3dd01209b9a0fd126705ca3461f"},"schema_version":"1.0"},"canonical_sha256":"f9fd520dcf9ac3436ffb098375d12bd27b34a0e46c43f22fa211601d1a9b2515","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:18.383158Z","signature_b64":"SW8ikZK09A901WSk2ww6aBhOYxInR3dtGHWuBYZpgSnKkXtVVROUQ2C7jFpZiuk/NZaVE2796HudZLZqSFjkBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9fd520dcf9ac3436ffb098375d12bd27b34a0e46c43f22fa211601d1a9b2515","last_reissued_at":"2026-07-05T11:25:18.382702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:18.382702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.17490","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SUvLDaWySHWSpFNVwwQNh7Z5ojdco3VbLDKKPHvvmTfmLsaprx309sWVsVm+ylabmJN1bgQh/hJm3OMb0N6qCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T14:22:50.734368Z"},"content_sha256":"8d8ea5303127225aaa23bdd7075d5b50ad73259bbdf2c305c9344b389557ad70","schema_version":"1.0","event_id":"sha256:8d8ea5303127225aaa23bdd7075d5b50ad73259bbdf2c305c9344b389557ad70"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:7H6VEDOPTLBUG373BGBXLUJL2J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Social Group Bias in AI Finance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-fin.EC"],"primary_cat":"econ.GN","authors_text":"Sophia Kazinnik, Thomas R. Cook","submitted_at":"2025-06-20T21:58:00Z","abstract_excerpt":"Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evaluates how models respond to simulated mortgage applicants identical in all attributes except race. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17490","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/2506.17490/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":"2026-07-05T11:25:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"meQzdUtY3ycfVscIeWNbKDAydGty3PM8/gJG2EtcGXLiD0LLF4SQ7+ZjXODmyTSNiMCfQ+HNHEHja+WSE620AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T14:22:50.734957Z"},"content_sha256":"2895dc8723ff025fdca391d8b76f21bf7770f20a1d98dbd616dbe6529411547b","schema_version":"1.0","event_id":"sha256:2895dc8723ff025fdca391d8b76f21bf7770f20a1d98dbd616dbe6529411547b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7H6VEDOPTLBUG373BGBXLUJL2J/bundle.json","state_url":"https://pith.science/pith/7H6VEDOPTLBUG373BGBXLUJL2J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7H6VEDOPTLBUG373BGBXLUJL2J/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":"2026-08-18T14:22:50Z","links":{"resolver":"https://pith.science/pith/7H6VEDOPTLBUG373BGBXLUJL2J","bundle":"https://pith.science/pith/7H6VEDOPTLBUG373BGBXLUJL2J/bundle.json","state":"https://pith.science/pith/7H6VEDOPTLBUG373BGBXLUJL2J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7H6VEDOPTLBUG373BGBXLUJL2J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7H6VEDOPTLBUG373BGBXLUJL2J","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":"e0659905f2032ca344c3778d0f5f5551c89aa3dd01209b9a0fd126705ca3461f","cross_cats_sorted":["q-fin.EC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2025-06-20T21:58:00Z","title_canon_sha256":"8de011ab6cf2bcd8d925b795d329bf2a2a17a5fb7162da552a3a82a512320cdc"},"schema_version":"1.0","source":{"id":"2506.17490","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.17490","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"arxiv_version","alias_value":"2506.17490v1","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17490","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"pith_short_12","alias_value":"7H6VEDOPTLBU","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"pith_short_16","alias_value":"7H6VEDOPTLBUG373","created_at":"2026-07-05T11:25:18Z"},{"alias_kind":"pith_short_8","alias_value":"7H6VEDOP","created_at":"2026-07-05T11:25:18Z"}],"graph_snapshots":[{"event_id":"sha256:2895dc8723ff025fdca391d8b76f21bf7770f20a1d98dbd616dbe6529411547b","target":"graph","created_at":"2026-07-05T11:25:18Z","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/2506.17490/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evaluates how models respond to simulated mortgage applicants identical in all attributes except race. Our ","authors_text":"Sophia Kazinnik, Thomas R. Cook","cross_cats":["q-fin.EC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2025-06-20T21:58:00Z","title":"Social Group Bias in AI Finance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17490","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":"sha256:8d8ea5303127225aaa23bdd7075d5b50ad73259bbdf2c305c9344b389557ad70","target":"record","created_at":"2026-07-05T11:25:18Z","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":"e0659905f2032ca344c3778d0f5f5551c89aa3dd01209b9a0fd126705ca3461f","cross_cats_sorted":["q-fin.EC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.GN","submitted_at":"2025-06-20T21:58:00Z","title_canon_sha256":"8de011ab6cf2bcd8d925b795d329bf2a2a17a5fb7162da552a3a82a512320cdc"},"schema_version":"1.0","source":{"id":"2506.17490","kind":"arxiv","version":1}},"canonical_sha256":"f9fd520dcf9ac3436ffb098375d12bd27b34a0e46c43f22fa211601d1a9b2515","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9fd520dcf9ac3436ffb098375d12bd27b34a0e46c43f22fa211601d1a9b2515","first_computed_at":"2026-07-05T11:25:18.382702Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:18.382702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SW8ikZK09A901WSk2ww6aBhOYxInR3dtGHWuBYZpgSnKkXtVVROUQ2C7jFpZiuk/NZaVE2796HudZLZqSFjkBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:18.383158Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.17490","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8d8ea5303127225aaa23bdd7075d5b50ad73259bbdf2c305c9344b389557ad70","sha256:2895dc8723ff025fdca391d8b76f21bf7770f20a1d98dbd616dbe6529411547b"],"state_sha256":"41bf1243746c745f291135c73d96f06d92bd57e0e75a4765bed1de59fc8cc67b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PyCIUi/iO4BOfBw59WVus/dECnQo325qP+Nu459xjYMaE6H+ZqR5Y4PyX2fkvz1y8O+N8LwttHvtnWLquqHMDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T14:22:50.738314Z","bundle_sha256":"09f3b38085643a9d2473fd27a7225c9ba67b57d79f5306422cfc02b64b43aee7"}}