{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6UKZCSJFVGJEPYDZJDWLQ4Q6UO","short_pith_number":"pith:6UKZCSJF","schema_version":"1.0","canonical_sha256":"f515914925a99247e07948ecb8721ea39cc26fd001bacbf76268d42a62b0689f","source":{"kind":"arxiv","id":"2409.20126","version":2},"attestation_state":"computed","paper":{"title":"DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Joana P. Gon\\c{c}alves, Yasin I. Tepeli","submitted_at":"2024-09-30T09:26:19Z","abstract_excerpt":"Fairness in machine learning seeks to mitigate model bias against individuals based on sensitive features such as sex or age, often caused by an uneven representation of the population in the training data due to selection bias. Notably, bias unascribed to sensitive features is challenging to identify and typically goes undiagnosed, despite its prominence in complex high-dimensional data from fields like computer vision and molecular biomedicine. Strategies to mitigate unidentified bias and evaluate mitigation methods are crucially needed, yet remain underexplored. We introduce: (i) Diverse Cl"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.20126","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-30T09:26:19Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"d89156d2489c28930204d88dbfc85dec7c5a8c6336f92fc0661a4a5745ab488d","abstract_canon_sha256":"5425f0012322ec3534836f5f0a0865871a01251a8f221ac1f47e17355e603ef6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:49.203684Z","signature_b64":"ncFlvfP+K53QWhGExxCIlzb4PRs1mrJNON/4X91CWtomUzyB1VUUhc2t5BTrrI/2YdDqm21DgEh5OLHZGdq0Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f515914925a99247e07948ecb8721ea39cc26fd001bacbf76268d42a62b0689f","last_reissued_at":"2026-07-05T09:17:49.203255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:49.203255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Joana P. Gon\\c{c}alves, Yasin I. Tepeli","submitted_at":"2024-09-30T09:26:19Z","abstract_excerpt":"Fairness in machine learning seeks to mitigate model bias against individuals based on sensitive features such as sex or age, often caused by an uneven representation of the population in the training data due to selection bias. Notably, bias unascribed to sensitive features is challenging to identify and typically goes undiagnosed, despite its prominence in complex high-dimensional data from fields like computer vision and molecular biomedicine. Strategies to mitigate unidentified bias and evaluate mitigation methods are crucially needed, yet remain underexplored. We introduce: (i) Diverse Cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.20126","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/2409.20126/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.20126","created_at":"2026-07-05T09:17:49.203311+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.20126v2","created_at":"2026-07-05T09:17:49.203311+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.20126","created_at":"2026-07-05T09:17:49.203311+00:00"},{"alias_kind":"pith_short_12","alias_value":"6UKZCSJFVGJE","created_at":"2026-07-05T09:17:49.203311+00:00"},{"alias_kind":"pith_short_16","alias_value":"6UKZCSJFVGJEPYDZ","created_at":"2026-07-05T09:17:49.203311+00:00"},{"alias_kind":"pith_short_8","alias_value":"6UKZCSJF","created_at":"2026-07-05T09:17:49.203311+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18442","citing_title":"Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO","json":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO.json","graph_json":"https://pith.science/api/pith-number/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/graph.json","events_json":"https://pith.science/api/pith-number/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/events.json","paper":"https://pith.science/paper/6UKZCSJF"},"agent_actions":{"view_html":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO","download_json":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO.json","view_paper":"https://pith.science/paper/6UKZCSJF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.20126&json=true","fetch_graph":"https://pith.science/api/pith-number/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/graph.json","fetch_events":"https://pith.science/api/pith-number/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/action/storage_attestation","attest_author":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/action/author_attestation","sign_citation":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/action/citation_signature","submit_replication":"https://pith.science/pith/6UKZCSJFVGJEPYDZJDWLQ4Q6UO/action/replication_record"}},"created_at":"2026-07-05T09:17:49.203311+00:00","updated_at":"2026-07-05T09:17:49.203311+00:00"}