{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:SQBZYHPGNQGJS5XGMNU36CITVJ","short_pith_number":"pith:SQBZYHPG","schema_version":"1.0","canonical_sha256":"94039c1de66c0c9976e66369bf0913aa60a5c134bb2492eb35ec1254f5284471","source":{"kind":"arxiv","id":"2008.10797","version":2},"attestation_state":"computed","paper":{"title":"The Fairness-Accuracy Pareto Front","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Marc Niethammer, Susan Wei","submitted_at":"2020-08-25T03:32:15Z","abstract_excerpt":"Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multi-objective optimization and seek the fairness-accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so-called linear scalarization scheme which has severe limitations in recover"},"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":"2008.10797","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-25T03:32:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"752ccd9855908ea5e6316777c19c6994ee0f0c8ada5f90858fffc209fe01df36","abstract_canon_sha256":"f586e967a5f241d71e780bfab55425ef940b969101f1b82f269e919fb4da64a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:33:18.916520Z","signature_b64":"4oB5Eu7UDuaHp1xgVeE6n/TO4jwIVekxC5Y5/jjeh5K0aAMDSeyu01Jp9Tl2d4AwNv+nOJy1vRt+bWzcNpPgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94039c1de66c0c9976e66369bf0913aa60a5c134bb2492eb35ec1254f5284471","last_reissued_at":"2026-07-05T03:33:18.915968Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:33:18.915968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Fairness-Accuracy Pareto Front","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Marc Niethammer, Susan Wei","submitted_at":"2020-08-25T03:32:15Z","abstract_excerpt":"Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multi-objective optimization and seek the fairness-accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so-called linear scalarization scheme which has severe limitations in recover"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.10797","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/2008.10797/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":"2008.10797","created_at":"2026-07-05T03:33:18.916035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.10797v2","created_at":"2026-07-05T03:33:18.916035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.10797","created_at":"2026-07-05T03:33:18.916035+00:00"},{"alias_kind":"pith_short_12","alias_value":"SQBZYHPGNQGJ","created_at":"2026-07-05T03:33:18.916035+00:00"},{"alias_kind":"pith_short_16","alias_value":"SQBZYHPGNQGJS5XG","created_at":"2026-07-05T03:33:18.916035+00:00"},{"alias_kind":"pith_short_8","alias_value":"SQBZYHPG","created_at":"2026-07-05T03:33:18.916035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09881","citing_title":"Toward Calibrated, Fair, and accurate Deepfake Detection","ref_index":139,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ","json":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ.json","graph_json":"https://pith.science/api/pith-number/SQBZYHPGNQGJS5XGMNU36CITVJ/graph.json","events_json":"https://pith.science/api/pith-number/SQBZYHPGNQGJS5XGMNU36CITVJ/events.json","paper":"https://pith.science/paper/SQBZYHPG"},"agent_actions":{"view_html":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ","download_json":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ.json","view_paper":"https://pith.science/paper/SQBZYHPG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.10797&json=true","fetch_graph":"https://pith.science/api/pith-number/SQBZYHPGNQGJS5XGMNU36CITVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/SQBZYHPGNQGJS5XGMNU36CITVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ/action/storage_attestation","attest_author":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ/action/author_attestation","sign_citation":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ/action/citation_signature","submit_replication":"https://pith.science/pith/SQBZYHPGNQGJS5XGMNU36CITVJ/action/replication_record"}},"created_at":"2026-07-05T03:33:18.916035+00:00","updated_at":"2026-07-05T03:33:18.916035+00:00"}