{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YYRPB6W4OUD4K67KJUSJXX5ZJ7","short_pith_number":"pith:YYRPB6W4","schema_version":"1.0","canonical_sha256":"c622f0fadc7507c57bea4d249bdfb94fdd03c90470a85353299f203903440749","source":{"kind":"arxiv","id":"1802.04422","version":1},"attestation_state":"computed","paper":{"title":"A comparative study of fairness-enhancing interventions in machine learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"stat.ML","authors_text":"Carlos Scheidegger, Derek Roth, Evan P. Hamilton, Sonam Choudhary, Sorelle A. Friedler, Suresh Venkatasubramanian","submitted_at":"2018-02-13T01:31:51Z","abstract_excerpt":"Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairne"},"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":"1802.04422","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2018-02-13T01:31:51Z","cross_cats_sorted":["cs.CY","cs.LG"],"title_canon_sha256":"34f81777503969c5082dc3feddda8fe97a67d8259161cbf89a6ee9345d7b5de6","abstract_canon_sha256":"ad895fdc06040ac3ed8ffabc8bede1a25afebdeee3eb7c542345e483b5aac817"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:44.254393Z","signature_b64":"xp1OPMPySMjGMscNMWIH4AnVGxKg+fwu1bfnCsbdk9mdLyA2azJmrwo6VlEH4vmy5SnsYeWNfh9455oXn9UFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c622f0fadc7507c57bea4d249bdfb94fdd03c90470a85353299f203903440749","last_reissued_at":"2026-05-18T00:23:44.253871Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:44.253871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A comparative study of fairness-enhancing interventions in machine learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"stat.ML","authors_text":"Carlos Scheidegger, Derek Roth, Evan P. Hamilton, Sonam Choudhary, Sorelle A. Friedler, Suresh Venkatasubramanian","submitted_at":"2018-02-13T01:31:51Z","abstract_excerpt":"Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04422","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":""},"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":"1802.04422","created_at":"2026-05-18T00:23:44.253947+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.04422v1","created_at":"2026-05-18T00:23:44.253947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04422","created_at":"2026-05-18T00:23:44.253947+00:00"},{"alias_kind":"pith_short_12","alias_value":"YYRPB6W4OUD4","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YYRPB6W4OUD4K67K","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YYRPB6W4","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.21465","citing_title":"Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7","json":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7.json","graph_json":"https://pith.science/api/pith-number/YYRPB6W4OUD4K67KJUSJXX5ZJ7/graph.json","events_json":"https://pith.science/api/pith-number/YYRPB6W4OUD4K67KJUSJXX5ZJ7/events.json","paper":"https://pith.science/paper/YYRPB6W4"},"agent_actions":{"view_html":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7","download_json":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7.json","view_paper":"https://pith.science/paper/YYRPB6W4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.04422&json=true","fetch_graph":"https://pith.science/api/pith-number/YYRPB6W4OUD4K67KJUSJXX5ZJ7/graph.json","fetch_events":"https://pith.science/api/pith-number/YYRPB6W4OUD4K67KJUSJXX5ZJ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7/action/storage_attestation","attest_author":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7/action/author_attestation","sign_citation":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7/action/citation_signature","submit_replication":"https://pith.science/pith/YYRPB6W4OUD4K67KJUSJXX5ZJ7/action/replication_record"}},"created_at":"2026-05-18T00:23:44.253947+00:00","updated_at":"2026-05-18T00:23:44.253947+00:00"}