{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CVAWS2CN45OKAHJEPSD5MSTF7H","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":"01ef9c38cd7972bb11a0b1a4bf1a9cdf682bf4100c453a7f32ed4d308e49e709","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-29T04:32:10Z","title_canon_sha256":"7a5c79571803f1e8950ee90eaa03731271429d4b89a7f26f1a4ce254b36c6396"},"schema_version":"1.0","source":{"id":"2206.14397","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.14397","created_at":"2026-07-05T07:39:56Z"},{"alias_kind":"arxiv_version","alias_value":"2206.14397v3","created_at":"2026-07-05T07:39:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.14397","created_at":"2026-07-05T07:39:56Z"},{"alias_kind":"pith_short_12","alias_value":"CVAWS2CN45OK","created_at":"2026-07-05T07:39:56Z"},{"alias_kind":"pith_short_16","alias_value":"CVAWS2CN45OKAHJE","created_at":"2026-07-05T07:39:56Z"},{"alias_kind":"pith_short_8","alias_value":"CVAWS2CN","created_at":"2026-07-05T07:39:56Z"}],"graph_snapshots":[{"event_id":"sha256:cb1f9c68cc8858a120c607e9a1e6acc53b381d69a7987c5876f6dce31e9cfdba","target":"graph","created_at":"2026-07-05T07:39:56Z","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/2206.14397/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problem is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in machine learning and healthcare disparities. We adopt a framework b","authors_text":"Mengnan Du, Na Zou, Qizhang Feng, Xia Hu","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-29T04:32:10Z","title":"Fair Machine Learning in Healthcare: A Review"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.14397","kind":"arxiv","version":3},"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":"sha256:641d3701d024bfbcf3b3b9b2072f58f5ffcbc6ec3d8076ccd6d055e584f0b659","target":"record","created_at":"2026-07-05T07:39:56Z","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":"01ef9c38cd7972bb11a0b1a4bf1a9cdf682bf4100c453a7f32ed4d308e49e709","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-29T04:32:10Z","title_canon_sha256":"7a5c79571803f1e8950ee90eaa03731271429d4b89a7f26f1a4ce254b36c6396"},"schema_version":"1.0","source":{"id":"2206.14397","kind":"arxiv","version":3}},"canonical_sha256":"154169684de75ca01d247c87d64a65f9e8e2e94338e143e7e3a94a7c16bf4ed1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"154169684de75ca01d247c87d64a65f9e8e2e94338e143e7e3a94a7c16bf4ed1","first_computed_at":"2026-07-05T07:39:56.139752Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:39:56.139752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wbBHWxnF5XCzS2ctjv3RCjWp2CeT/07ZQ1sICw8mj7zA1mcsdb9+3FIO6xesImPDEMaASr3sHme2ASIlkB6EDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:39:56.140249Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.14397","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:641d3701d024bfbcf3b3b9b2072f58f5ffcbc6ec3d8076ccd6d055e584f0b659","sha256:cb1f9c68cc8858a120c607e9a1e6acc53b381d69a7987c5876f6dce31e9cfdba"],"state_sha256":"0c8efac83bcbbf52c7fb57914ae14e989917e536c40394899e037f197adb449a"}