{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KAUT76HD5BZTPDUIQCWWMZZ7PW","short_pith_number":"pith:KAUT76HD","schema_version":"1.0","canonical_sha256":"50293ff8e3e873378e8880ad66673f7d9338dc2955d9c38bf8c81ba29854c8e5","source":{"kind":"arxiv","id":"2209.15605","version":8},"attestation_state":"computed","paper":{"title":"Bias Mimicking: A Simple Sampling Approach for Bias Mitigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bryan A. Plummer, Kate Saenko, Maan Qraitem","submitted_at":"2022-09-30T17:33:00Z","abstract_excerpt":"Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups $B$ (\\eg Female) within class labels $Y$ (\\eg Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (\\eg Undersampling, Upweighting), which can often be implemented in a single lin"},"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":"2209.15605","kind":"arxiv","version":8},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-30T17:33:00Z","cross_cats_sorted":[],"title_canon_sha256":"74d2ad74a3a328ab43b8157b4d7fec2dc20dfc2b66be79f6f6d52d3e89bc5552","abstract_canon_sha256":"f97c70b51fc252ba1dede4b30ca2696a83d1d2c9e7b78a1ab384c5426868d987"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:04:51.928661Z","signature_b64":"ZBJudbVp2H7niwGzSrpIsERLj43F8b/9lVIC2t/kPieZ9hbMnbt9Ktjz4w+LDRRUuJMPLGjUTrmNIuCaLpj6Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50293ff8e3e873378e8880ad66673f7d9338dc2955d9c38bf8c81ba29854c8e5","last_reissued_at":"2026-07-05T06:04:51.928227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:04:51.928227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bias Mimicking: A Simple Sampling Approach for Bias Mitigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bryan A. Plummer, Kate Saenko, Maan Qraitem","submitted_at":"2022-09-30T17:33:00Z","abstract_excerpt":"Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups $B$ (\\eg Female) within class labels $Y$ (\\eg Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (\\eg Undersampling, Upweighting), which can often be implemented in a single lin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.15605","kind":"arxiv","version":8},"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/2209.15605/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":"2209.15605","created_at":"2026-07-05T06:04:51.928285+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.15605v8","created_at":"2026-07-05T06:04:51.928285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.15605","created_at":"2026-07-05T06:04:51.928285+00:00"},{"alias_kind":"pith_short_12","alias_value":"KAUT76HD5BZT","created_at":"2026-07-05T06:04:51.928285+00:00"},{"alias_kind":"pith_short_16","alias_value":"KAUT76HD5BZTPDUI","created_at":"2026-07-05T06:04:51.928285+00:00"},{"alias_kind":"pith_short_8","alias_value":"KAUT76HD","created_at":"2026-07-05T06:04:51.928285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW","json":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW.json","graph_json":"https://pith.science/api/pith-number/KAUT76HD5BZTPDUIQCWWMZZ7PW/graph.json","events_json":"https://pith.science/api/pith-number/KAUT76HD5BZTPDUIQCWWMZZ7PW/events.json","paper":"https://pith.science/paper/KAUT76HD"},"agent_actions":{"view_html":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW","download_json":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW.json","view_paper":"https://pith.science/paper/KAUT76HD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.15605&json=true","fetch_graph":"https://pith.science/api/pith-number/KAUT76HD5BZTPDUIQCWWMZZ7PW/graph.json","fetch_events":"https://pith.science/api/pith-number/KAUT76HD5BZTPDUIQCWWMZZ7PW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW/action/storage_attestation","attest_author":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW/action/author_attestation","sign_citation":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW/action/citation_signature","submit_replication":"https://pith.science/pith/KAUT76HD5BZTPDUIQCWWMZZ7PW/action/replication_record"}},"created_at":"2026-07-05T06:04:51.928285+00:00","updated_at":"2026-07-05T06:04:51.928285+00:00"}