{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:Y7XFDW34CGPKB5KWUMNPZNOAJW","short_pith_number":"pith:Y7XFDW34","schema_version":"1.0","canonical_sha256":"c7ee51db7c119ea0f556a31afcb5c04da01f35ae0c064bff0543ac7739548ea3","source":{"kind":"arxiv","id":"1807.00905","version":2},"attestation_state":"computed","paper":{"title":"Learning under selective labels in the presence of expert consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandra Chouldechova, Artur Dubrawski, Maria De-Arteaga","submitted_at":"2018-07-02T21:48:59Z","abstract_excerpt":"We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data a"},"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":"1807.00905","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-02T21:48:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5eee87654da6568b3696616062dce2bd787d891b74a0fdee0be09cfc17bfd04f","abstract_canon_sha256":"f8e774aa38316778ecfab90fbe602f8eb6790bf50f97d8f11ca896a5940ece00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:28.808000Z","signature_b64":"XFZ/S43Y12u65JVZEwU75YDOP7Mw91UO8TeldapiDG9wW++YdBZfFhBMoE4Zw0pyX2dsMLgtxtWB14LPfpuSBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7ee51db7c119ea0f556a31afcb5c04da01f35ae0c064bff0543ac7739548ea3","last_reissued_at":"2026-05-18T00:11:28.807658Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:28.807658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning under selective labels in the presence of expert consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandra Chouldechova, Artur Dubrawski, Maria De-Arteaga","submitted_at":"2018-07-02T21:48:59Z","abstract_excerpt":"We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00905","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":""},"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":"1807.00905","created_at":"2026-05-18T00:11:28.807711+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.00905v2","created_at":"2026-05-18T00:11:28.807711+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00905","created_at":"2026-05-18T00:11:28.807711+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y7XFDW34CGPK","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y7XFDW34CGPKB5KW","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y7XFDW34","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.21711","citing_title":"Fairness under uncertainty in sequential decisions","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW","json":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW.json","graph_json":"https://pith.science/api/pith-number/Y7XFDW34CGPKB5KWUMNPZNOAJW/graph.json","events_json":"https://pith.science/api/pith-number/Y7XFDW34CGPKB5KWUMNPZNOAJW/events.json","paper":"https://pith.science/paper/Y7XFDW34"},"agent_actions":{"view_html":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW","download_json":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW.json","view_paper":"https://pith.science/paper/Y7XFDW34","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.00905&json=true","fetch_graph":"https://pith.science/api/pith-number/Y7XFDW34CGPKB5KWUMNPZNOAJW/graph.json","fetch_events":"https://pith.science/api/pith-number/Y7XFDW34CGPKB5KWUMNPZNOAJW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW/action/storage_attestation","attest_author":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW/action/author_attestation","sign_citation":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW/action/citation_signature","submit_replication":"https://pith.science/pith/Y7XFDW34CGPKB5KWUMNPZNOAJW/action/replication_record"}},"created_at":"2026-05-18T00:11:28.807711+00:00","updated_at":"2026-05-18T00:11:28.807711+00:00"}