{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3K7FWKEE3WX62RJFIU35V3BIB7","short_pith_number":"pith:3K7FWKEE","canonical_record":{"source":{"id":"2210.02516","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-05T19:23:29Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"365999ee41330c25a423970493add144ebe6e37c39bb271dd505a4754cbaa96a","abstract_canon_sha256":"8d4acf9622f7b330a8cb91f73d941062884e02b743f486d64d21d0a62ff401b0"},"schema_version":"1.0"},"canonical_sha256":"dabe5b2884ddafed45254537daec280fed692bfe8a28377351c931b4d351a101","source":{"kind":"arxiv","id":"2210.02516","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.02516","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"arxiv_version","alias_value":"2210.02516v1","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02516","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"pith_short_12","alias_value":"3K7FWKEE3WX6","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"pith_short_16","alias_value":"3K7FWKEE3WX62RJF","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"pith_short_8","alias_value":"3K7FWKEE","created_at":"2026-07-05T05:04:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3K7FWKEE3WX62RJFIU35V3BIB7","target":"record","payload":{"canonical_record":{"source":{"id":"2210.02516","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-05T19:23:29Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"365999ee41330c25a423970493add144ebe6e37c39bb271dd505a4754cbaa96a","abstract_canon_sha256":"8d4acf9622f7b330a8cb91f73d941062884e02b743f486d64d21d0a62ff401b0"},"schema_version":"1.0"},"canonical_sha256":"dabe5b2884ddafed45254537daec280fed692bfe8a28377351c931b4d351a101","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:04.302083Z","signature_b64":"YoDduGOUHIeRA3rJcsSqV866VLwW9LSrBpSsfeOC1NheRbnPVPIjJHldyKCDFn4S81kOfCh0cFnNV01zziFXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dabe5b2884ddafed45254537daec280fed692bfe8a28377351c931b4d351a101","last_reissued_at":"2026-07-05T05:04:04.301662Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:04.301662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.02516","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":"2026-07-05T05:04:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pyKag1zj81VwFDCzCWNn3xm/O+84FcuUBtD/5+U/sJKDXqSV3+S2U10VzqHuqGRrg+OcokokaSl4FsAQ1ycKAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T09:24:46.152146Z"},"content_sha256":"1fb578626ed3bced38344867da11469e6eb6376d2079707385745e8e8725a660","schema_version":"1.0","event_id":"sha256:1fb578626ed3bced38344867da11469e6eb6376d2079707385745e8e8725a660"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3K7FWKEE3WX62RJFIU35V3BIB7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"I. Elizabeth Kumar, John P. Dickerson, Keegan E. Hines","submitted_at":"2022-10-05T19:23:29Z","abstract_excerpt":"Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today. Today, machine learning algorithms, sometimes trained on alternative data, are increasingly being used to determine access to credit, yet research has shown that machine learning can encode many different versions of \"unfairness,\" thus raising the concern that banks and other financial institutions could -- potentially unwittingly -- engage in illegal discrimination through the use of this technology. In the US, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02516","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/2210.02516/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":"2026-07-05T05:04:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+GMSmiqUowGyI0ztMTLqzll8XRgodnqrz4mxQXChe8A2PDQSDOW9ca7Q8SpyqSKGiEMByyB/waJS7C0kvaSdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T09:24:46.152582Z"},"content_sha256":"897b2c2977a79b3ae27a1229e4f955a349a0330e13d5269c312d03a98ed8f156","schema_version":"1.0","event_id":"sha256:897b2c2977a79b3ae27a1229e4f955a349a0330e13d5269c312d03a98ed8f156"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3K7FWKEE3WX62RJFIU35V3BIB7/bundle.json","state_url":"https://pith.science/pith/3K7FWKEE3WX62RJFIU35V3BIB7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3K7FWKEE3WX62RJFIU35V3BIB7/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":"2026-08-14T09:24:46Z","links":{"resolver":"https://pith.science/pith/3K7FWKEE3WX62RJFIU35V3BIB7","bundle":"https://pith.science/pith/3K7FWKEE3WX62RJFIU35V3BIB7/bundle.json","state":"https://pith.science/pith/3K7FWKEE3WX62RJFIU35V3BIB7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3K7FWKEE3WX62RJFIU35V3BIB7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3K7FWKEE3WX62RJFIU35V3BIB7","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":"8d4acf9622f7b330a8cb91f73d941062884e02b743f486d64d21d0a62ff401b0","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-05T19:23:29Z","title_canon_sha256":"365999ee41330c25a423970493add144ebe6e37c39bb271dd505a4754cbaa96a"},"schema_version":"1.0","source":{"id":"2210.02516","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.02516","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"arxiv_version","alias_value":"2210.02516v1","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02516","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"pith_short_12","alias_value":"3K7FWKEE3WX6","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"pith_short_16","alias_value":"3K7FWKEE3WX62RJF","created_at":"2026-07-05T05:04:04Z"},{"alias_kind":"pith_short_8","alias_value":"3K7FWKEE","created_at":"2026-07-05T05:04:04Z"}],"graph_snapshots":[{"event_id":"sha256:897b2c2977a79b3ae27a1229e4f955a349a0330e13d5269c312d03a98ed8f156","target":"graph","created_at":"2026-07-05T05:04:04Z","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/2210.02516/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today. Today, machine learning algorithms, sometimes trained on alternative data, are increasingly being used to determine access to credit, yet research has shown that machine learning can encode many different versions of \"unfairness,\" thus raising the concern that banks and other financial institutions could -- potentially unwittingly -- engage in illegal discrimination through the use of this technology. In the US, ","authors_text":"I. Elizabeth Kumar, John P. Dickerson, Keegan E. Hines","cross_cats":["cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-05T19:23:29Z","title":"Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02516","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":"sha256:1fb578626ed3bced38344867da11469e6eb6376d2079707385745e8e8725a660","target":"record","created_at":"2026-07-05T05:04:04Z","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":"8d4acf9622f7b330a8cb91f73d941062884e02b743f486d64d21d0a62ff401b0","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-05T19:23:29Z","title_canon_sha256":"365999ee41330c25a423970493add144ebe6e37c39bb271dd505a4754cbaa96a"},"schema_version":"1.0","source":{"id":"2210.02516","kind":"arxiv","version":1}},"canonical_sha256":"dabe5b2884ddafed45254537daec280fed692bfe8a28377351c931b4d351a101","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dabe5b2884ddafed45254537daec280fed692bfe8a28377351c931b4d351a101","first_computed_at":"2026-07-05T05:04:04.301662Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:04.301662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YoDduGOUHIeRA3rJcsSqV866VLwW9LSrBpSsfeOC1NheRbnPVPIjJHldyKCDFn4S81kOfCh0cFnNV01zziFXAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:04.302083Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.02516","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1fb578626ed3bced38344867da11469e6eb6376d2079707385745e8e8725a660","sha256:897b2c2977a79b3ae27a1229e4f955a349a0330e13d5269c312d03a98ed8f156"],"state_sha256":"8a916d57550236cfaed3ce43f8630e695668d843303a5bd93321a5096abc08f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pJAWCRd0/8N5c9upmPuF4KeShuX9xlznIuAHs8BU6pKuuV7yxb7eLKwFLXyq6KVEpPNDGzJvADt3Ga+D86hCAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T09:24:46.156757Z","bundle_sha256":"4d776420bef0b8d5d9435dfc91d6e70ec6f70c718a9e3daecf6d96c33f55a7ae"}}