{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KC5NJKEQQE2NEJYVHXTV5PI4HO","short_pith_number":"pith:KC5NJKEQ","schema_version":"1.0","canonical_sha256":"50bad4a8908134d227153de75ebd1c3b8838432d76bed2bca7fde58ae0bb7d08","source":{"kind":"arxiv","id":"1903.12220","version":1},"attestation_state":"computed","paper":{"title":"The Algorithmic Automation Problem: Prediction, Triage, and Human Effort","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Greg Corrado, Jon Kleinberg, Katy Blumer, Maithra Raghu, Sendhil Mullainathan, Ziad Obermeyer","submitted_at":"2019-03-28T18:53:58Z","abstract_excerpt":"In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains. The discussion around these developments, however, has implicitly equated the specific task of prediction with the general task of automation. We argue here that automation is broader than just a comparison of human versus algorithmic performance on a task; it also involves the decision of which instances of the task to give to the algorithm in the first place. We develop a general framework that "},"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":"1903.12220","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T18:53:58Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"93432944d1a87da01a00ae78145e7c6f9f1dd0ddf0c7c51ec5ca47d9bb54236b","abstract_canon_sha256":"32a88fba5da5ec6980d8eb0981e42e35a64dbf47346fe69e96360e261b43acc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:54.563303Z","signature_b64":"04dDzgFKt6S+XKGcfP9ToqK8RtlHFoqC2s/Zg+HbHwOymQJUHb5mQELaBPdoqhxjLYNgH1W/CjCpLo0glOHnAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50bad4a8908134d227153de75ebd1c3b8838432d76bed2bca7fde58ae0bb7d08","last_reissued_at":"2026-05-17T23:49:54.562855Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:54.562855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Algorithmic Automation Problem: Prediction, Triage, and Human Effort","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Greg Corrado, Jon Kleinberg, Katy Blumer, Maithra Raghu, Sendhil Mullainathan, Ziad Obermeyer","submitted_at":"2019-03-28T18:53:58Z","abstract_excerpt":"In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains. The discussion around these developments, however, has implicitly equated the specific task of prediction with the general task of automation. We argue here that automation is broader than just a comparison of human versus algorithmic performance on a task; it also involves the decision of which instances of the task to give to the algorithm in the first place. We develop a general framework that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.12220","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":"1903.12220","created_at":"2026-05-17T23:49:54.562925+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.12220v1","created_at":"2026-05-17T23:49:54.562925+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.12220","created_at":"2026-05-17T23:49:54.562925+00:00"},{"alias_kind":"pith_short_12","alias_value":"KC5NJKEQQE2N","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KC5NJKEQQE2NEJYV","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KC5NJKEQ","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2402.06287","citing_title":"AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems","ref_index":123,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27723","citing_title":"Optimized Deferral for Imbalanced Settings","ref_index":102,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20409","citing_title":"Calibrating conditional risk","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13285","citing_title":"L2D-Clinical: Learning to Defer for Adaptive Model Selection in Clinical Text Classification","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07805","citing_title":"Flexible Routing via Uncertainty Decomposition","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08024","citing_title":"MPD$^2$-Router: Mask-aware Multi-expert Prior-regularized Dual-head Deferral Router in Glaucoma Screening and Diagnosis","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14980","citing_title":"Hybrid Decision Making via Conformal VLM-generated Guidance","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO","json":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO.json","graph_json":"https://pith.science/api/pith-number/KC5NJKEQQE2NEJYVHXTV5PI4HO/graph.json","events_json":"https://pith.science/api/pith-number/KC5NJKEQQE2NEJYVHXTV5PI4HO/events.json","paper":"https://pith.science/paper/KC5NJKEQ"},"agent_actions":{"view_html":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO","download_json":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO.json","view_paper":"https://pith.science/paper/KC5NJKEQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.12220&json=true","fetch_graph":"https://pith.science/api/pith-number/KC5NJKEQQE2NEJYVHXTV5PI4HO/graph.json","fetch_events":"https://pith.science/api/pith-number/KC5NJKEQQE2NEJYVHXTV5PI4HO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO/action/storage_attestation","attest_author":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO/action/author_attestation","sign_citation":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO/action/citation_signature","submit_replication":"https://pith.science/pith/KC5NJKEQQE2NEJYVHXTV5PI4HO/action/replication_record"}},"created_at":"2026-05-17T23:49:54.562925+00:00","updated_at":"2026-05-17T23:49:54.562925+00:00"}