{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YF7SO4JPDXL3YN3HNW4LGXU3NF","short_pith_number":"pith:YF7SO4JP","schema_version":"1.0","canonical_sha256":"c17f27712f1dd7bc37676db8b35e9b69568865485cba066065418c707358d545","source":{"kind":"arxiv","id":"2306.07261","version":5},"attestation_state":"computed","paper":{"title":"Unprocessing Seven Years of Algorithmic Fairness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Andr\\'e F. Cruz, Moritz Hardt","submitted_at":"2023-06-12T17:44:15Z","abstract_excerpt":"Seven years ago, researchers proposed a postprocessing method to equalize the error rates of a model across different demographic groups. The work launched hundreds of papers purporting to improve over the postprocessing baseline. We empirically evaluate these claims through thousands of model evaluations on several tabular datasets. We find that the fairness-accuracy Pareto frontier achieved by postprocessing contains all other methods we were feasibly able to evaluate. In doing so, we address two common methodological errors that have confounded previous observations. One relates to the comp"},"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":"2306.07261","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-12T17:44:15Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"1c4b7a96171388af39045ad0c7b825f7c5cd524d06af44ffd019ed95aff03afe","abstract_canon_sha256":"cf9ebb805183f5b2627c95a716c6e69da0453fb7e0756ecd0e9e9749f02f1b2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:15.571777Z","signature_b64":"h3hDrLEgpcEEwqStbS02cvmo20o/cUSszHDc4hSe6uXkVKvttIPA8iKMGwI82W7cB8JfPzSlFsbfkHB3zQ5HAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c17f27712f1dd7bc37676db8b35e9b69568865485cba066065418c707358d545","last_reissued_at":"2026-07-05T07:56:15.571325Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:15.571325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unprocessing Seven Years of Algorithmic Fairness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Andr\\'e F. Cruz, Moritz Hardt","submitted_at":"2023-06-12T17:44:15Z","abstract_excerpt":"Seven years ago, researchers proposed a postprocessing method to equalize the error rates of a model across different demographic groups. The work launched hundreds of papers purporting to improve over the postprocessing baseline. We empirically evaluate these claims through thousands of model evaluations on several tabular datasets. We find that the fairness-accuracy Pareto frontier achieved by postprocessing contains all other methods we were feasibly able to evaluate. In doing so, we address two common methodological errors that have confounded previous observations. One relates to the comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07261","kind":"arxiv","version":5},"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/2306.07261/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":"2306.07261","created_at":"2026-07-05T07:56:15.571381+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.07261v5","created_at":"2026-07-05T07:56:15.571381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07261","created_at":"2026-07-05T07:56:15.571381+00:00"},{"alias_kind":"pith_short_12","alias_value":"YF7SO4JPDXL3","created_at":"2026-07-05T07:56:15.571381+00:00"},{"alias_kind":"pith_short_16","alias_value":"YF7SO4JPDXL3YN3H","created_at":"2026-07-05T07:56:15.571381+00:00"},{"alias_kind":"pith_short_8","alias_value":"YF7SO4JP","created_at":"2026-07-05T07:56:15.571381+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23515","citing_title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF","json":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF.json","graph_json":"https://pith.science/api/pith-number/YF7SO4JPDXL3YN3HNW4LGXU3NF/graph.json","events_json":"https://pith.science/api/pith-number/YF7SO4JPDXL3YN3HNW4LGXU3NF/events.json","paper":"https://pith.science/paper/YF7SO4JP"},"agent_actions":{"view_html":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF","download_json":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF.json","view_paper":"https://pith.science/paper/YF7SO4JP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.07261&json=true","fetch_graph":"https://pith.science/api/pith-number/YF7SO4JPDXL3YN3HNW4LGXU3NF/graph.json","fetch_events":"https://pith.science/api/pith-number/YF7SO4JPDXL3YN3HNW4LGXU3NF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF/action/storage_attestation","attest_author":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF/action/author_attestation","sign_citation":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF/action/citation_signature","submit_replication":"https://pith.science/pith/YF7SO4JPDXL3YN3HNW4LGXU3NF/action/replication_record"}},"created_at":"2026-07-05T07:56:15.571381+00:00","updated_at":"2026-07-05T07:56:15.571381+00:00"}