{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:K4QVSN5P2AR5HOGF2GBQCHF4F7","short_pith_number":"pith:K4QVSN5P","canonical_record":{"source":{"id":"2305.06969","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-11T16:49:22Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"99d4aec099baad1c918452e537e330a6839186657a53e9326e5700bf8e7d7329","abstract_canon_sha256":"98ef7be964dbda2da75383899a1ba9b84b616b1f36b2e1272297e564e3f0b2ef"},"schema_version":"1.0"},"canonical_sha256":"57215937afd023d3b8c5d183011cbc2fdb07e532eaf67af0f743c93d4da489c1","source":{"kind":"arxiv","id":"2305.06969","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.06969","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"arxiv_version","alias_value":"2305.06969v2","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.06969","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"pith_short_12","alias_value":"K4QVSN5P2AR5","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"pith_short_16","alias_value":"K4QVSN5P2AR5HOGF","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"pith_short_8","alias_value":"K4QVSN5P","created_at":"2026-07-05T06:40:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:K4QVSN5P2AR5HOGF2GBQCHF4F7","target":"record","payload":{"canonical_record":{"source":{"id":"2305.06969","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-11T16:49:22Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"99d4aec099baad1c918452e537e330a6839186657a53e9326e5700bf8e7d7329","abstract_canon_sha256":"98ef7be964dbda2da75383899a1ba9b84b616b1f36b2e1272297e564e3f0b2ef"},"schema_version":"1.0"},"canonical_sha256":"57215937afd023d3b8c5d183011cbc2fdb07e532eaf67af0f743c93d4da489c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:33.953296Z","signature_b64":"b3zCbP2rF1UPV9uFlcT8Sjk9AgJD72BaTICNik90SRx39dn7TNpe2gU1IEdVAo6WkS7aKaD+C6sK188Z6CInBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57215937afd023d3b8c5d183011cbc2fdb07e532eaf67af0f743c93d4da489c1","last_reissued_at":"2026-07-05T06:40:33.952816Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:33.952816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.06969","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":"2026-07-05T06:40:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7HGckzsCxIsAeGH76KbkJdxNfZVZkC7d6NnuolVOh2JP/+GdRmlZzV0oHIwLD2Rfjikm5DaDeAflblYv+D/tDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:07:23.495480Z"},"content_sha256":"4d64e59b8994bb0a329c2675be47452922c640f6478b08f7c28c1006b02b2535","schema_version":"1.0","event_id":"sha256:4d64e59b8994bb0a329c2675be47452922c640f6478b08f7c28c1006b02b2535"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:K4QVSN5P2AR5HOGF2GBQCHF4F7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Lu Cheng, Usman Gohar","submitted_at":"2023-05-11T16:49:22Z","abstract_excerpt":"The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.06969","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/2305.06969/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":"2026-07-05T06:40:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UKuy0I0HpOtQwyjcVveZY33VaT32RWzSyY22E4P/zkUQQZg8lsRuoj1XlpiymczwBuXuKLrtd49rbPaE0XwSAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:07:23.495991Z"},"content_sha256":"94a8117408c527701e3f258d935d9f8a02da0e154013556b68d31e0914a2e72f","schema_version":"1.0","event_id":"sha256:94a8117408c527701e3f258d935d9f8a02da0e154013556b68d31e0914a2e72f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7/bundle.json","state_url":"https://pith.science/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7/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":"2026-08-08T22:07:23Z","links":{"resolver":"https://pith.science/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7","bundle":"https://pith.science/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7/bundle.json","state":"https://pith.science/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K4QVSN5P2AR5HOGF2GBQCHF4F7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:K4QVSN5P2AR5HOGF2GBQCHF4F7","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":"98ef7be964dbda2da75383899a1ba9b84b616b1f36b2e1272297e564e3f0b2ef","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-11T16:49:22Z","title_canon_sha256":"99d4aec099baad1c918452e537e330a6839186657a53e9326e5700bf8e7d7329"},"schema_version":"1.0","source":{"id":"2305.06969","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.06969","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"arxiv_version","alias_value":"2305.06969v2","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.06969","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"pith_short_12","alias_value":"K4QVSN5P2AR5","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"pith_short_16","alias_value":"K4QVSN5P2AR5HOGF","created_at":"2026-07-05T06:40:33Z"},{"alias_kind":"pith_short_8","alias_value":"K4QVSN5P","created_at":"2026-07-05T06:40:33Z"}],"graph_snapshots":[{"event_id":"sha256:94a8117408c527701e3f258d935d9f8a02da0e154013556b68d31e0914a2e72f","target":"graph","created_at":"2026-07-05T06:40:33Z","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/2305.06969/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions","authors_text":"Lu Cheng, Usman Gohar","cross_cats":["cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-11T16:49:22Z","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.06969","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":"sha256:4d64e59b8994bb0a329c2675be47452922c640f6478b08f7c28c1006b02b2535","target":"record","created_at":"2026-07-05T06:40:33Z","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":"98ef7be964dbda2da75383899a1ba9b84b616b1f36b2e1272297e564e3f0b2ef","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-11T16:49:22Z","title_canon_sha256":"99d4aec099baad1c918452e537e330a6839186657a53e9326e5700bf8e7d7329"},"schema_version":"1.0","source":{"id":"2305.06969","kind":"arxiv","version":2}},"canonical_sha256":"57215937afd023d3b8c5d183011cbc2fdb07e532eaf67af0f743c93d4da489c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57215937afd023d3b8c5d183011cbc2fdb07e532eaf67af0f743c93d4da489c1","first_computed_at":"2026-07-05T06:40:33.952816Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:40:33.952816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b3zCbP2rF1UPV9uFlcT8Sjk9AgJD72BaTICNik90SRx39dn7TNpe2gU1IEdVAo6WkS7aKaD+C6sK188Z6CInBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:40:33.953296Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.06969","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d64e59b8994bb0a329c2675be47452922c640f6478b08f7c28c1006b02b2535","sha256:94a8117408c527701e3f258d935d9f8a02da0e154013556b68d31e0914a2e72f"],"state_sha256":"c14b27d47cc4eca35ce13f6e10dc15692df126a13d1ff3e7a8747c2b7cd29713"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RYHD/+embhMUN8FLVoDsZ+nDqikqsATJwUGpzETw0jNtNrtNLxhJVE2XzG0hH/DEErys2464kK410yeL3s7DAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:07:23.499782Z","bundle_sha256":"52764aec143533e84485d320d0498c94a5c9b3e845c93164fd0735c137fe39eb"}}