{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2FH26MUK2LC7DBSKJHSU26PQTQ","short_pith_number":"pith:2FH26MUK","schema_version":"1.0","canonical_sha256":"d14faf328ad2c5f1864a49e54d79f09c270a4bc76b36140e9728bf3856eb29d2","source":{"kind":"arxiv","id":"2211.00168","version":1},"attestation_state":"computed","paper":{"title":"Improving Fairness in Image Classification via Sketching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Lin Gu, Ruichen Yao, Xiaoxiao Li, Ziteng Cui","submitted_at":"2022-10-31T22:26:32Z","abstract_excerpt":"Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair predictions when the training data are collected from different sub-populations with different attributes (i.e. color, sex, age), leading to biased DNN predictions. We notice that such a troubling phenomenon is often caused by data itself, which means that bias information is encoded to the DNN along with the useful information (i.e. class information, semantic information). Therefore, we propose to use sketching to handle this p"},"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":"2211.00168","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-31T22:26:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5cfe9dc51acc47b441f93c3d5dc1031e8cab7816d9736052bfe90a350acaca4a","abstract_canon_sha256":"e661032edac6e4de09dd00d42c532ef3531a63f9515aea04a43305f34de92593"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:19.342943Z","signature_b64":"blhn6wilcUbfb8IVADucecdhwBcmLpGCbimr0/Bl/LhxUh1UiPiJ/cQOljOSM/W6s7YjiuaaoWfzdKW1YLSLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d14faf328ad2c5f1864a49e54d79f09c270a4bc76b36140e9728bf3856eb29d2","last_reissued_at":"2026-07-05T05:12:19.342533Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:19.342533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Fairness in Image Classification via Sketching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Lin Gu, Ruichen Yao, Xiaoxiao Li, Ziteng Cui","submitted_at":"2022-10-31T22:26:32Z","abstract_excerpt":"Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair predictions when the training data are collected from different sub-populations with different attributes (i.e. color, sex, age), leading to biased DNN predictions. We notice that such a troubling phenomenon is often caused by data itself, which means that bias information is encoded to the DNN along with the useful information (i.e. class information, semantic information). Therefore, we propose to use sketching to handle this p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00168","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/2211.00168/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":"2211.00168","created_at":"2026-07-05T05:12:19.342588+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.00168v1","created_at":"2026-07-05T05:12:19.342588+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00168","created_at":"2026-07-05T05:12:19.342588+00:00"},{"alias_kind":"pith_short_12","alias_value":"2FH26MUK2LC7","created_at":"2026-07-05T05:12:19.342588+00:00"},{"alias_kind":"pith_short_16","alias_value":"2FH26MUK2LC7DBSK","created_at":"2026-07-05T05:12:19.342588+00:00"},{"alias_kind":"pith_short_8","alias_value":"2FH26MUK","created_at":"2026-07-05T05:12:19.342588+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27148","citing_title":"Landseer: Exploring the Machine Learning Defense Landscape","ref_index":116,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ","json":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ.json","graph_json":"https://pith.science/api/pith-number/2FH26MUK2LC7DBSKJHSU26PQTQ/graph.json","events_json":"https://pith.science/api/pith-number/2FH26MUK2LC7DBSKJHSU26PQTQ/events.json","paper":"https://pith.science/paper/2FH26MUK"},"agent_actions":{"view_html":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ","download_json":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ.json","view_paper":"https://pith.science/paper/2FH26MUK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.00168&json=true","fetch_graph":"https://pith.science/api/pith-number/2FH26MUK2LC7DBSKJHSU26PQTQ/graph.json","fetch_events":"https://pith.science/api/pith-number/2FH26MUK2LC7DBSKJHSU26PQTQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ/action/storage_attestation","attest_author":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ/action/author_attestation","sign_citation":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ/action/citation_signature","submit_replication":"https://pith.science/pith/2FH26MUK2LC7DBSKJHSU26PQTQ/action/replication_record"}},"created_at":"2026-07-05T05:12:19.342588+00:00","updated_at":"2026-07-05T05:12:19.342588+00:00"}