{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3ZG3ATS37K2ABTKGKX4FTFR5ZC","short_pith_number":"pith:3ZG3ATS3","schema_version":"1.0","canonical_sha256":"de4db04e5bfab400cd4655f859963dc8a785ff7bf3309de2de05388cfc25e268","source":{"kind":"arxiv","id":"2312.13218","version":1},"attestation_state":"computed","paper":{"title":"FiFAR: A Fraud Detection Dataset for Learning to Defer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Diogo Leit\\~ao, Jean V. Alves, Marco O. P. Sampaio, M\\'ario A. T. Figueiredo, Pedro Bizarro, Pedro Saleiro, S\\'ergio Jesus","submitted_at":"2023-12-20T17:36:36Z","abstract_excerpt":"Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availability and domain-specific concerns introduce difficulties, while obtaining human predictions for training and evaluation is costly. Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI tea"},"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":"2312.13218","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-20T17:36:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b0f57f738501ebd538047200a100e95af4638e2c2423df7c4306cac963258cfe","abstract_canon_sha256":"4e8ed5b79cdec820de9199d80bbd1f48d82a25e31d12e2f58d5781b04182c649"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:33.574988Z","signature_b64":"PqPxYD7A69QE7IrHUqSeGo+fwvC6QrKVpXTmvzkFVb+QgnsJoqr+LWliL7hdRgs9BmUYqJLC6cDSf9le8NCtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de4db04e5bfab400cd4655f859963dc8a785ff7bf3309de2de05388cfc25e268","last_reissued_at":"2026-07-05T07:26:33.574500Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:33.574500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FiFAR: A Fraud Detection Dataset for Learning to Defer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Diogo Leit\\~ao, Jean V. Alves, Marco O. P. Sampaio, M\\'ario A. T. Figueiredo, Pedro Bizarro, Pedro Saleiro, S\\'ergio Jesus","submitted_at":"2023-12-20T17:36:36Z","abstract_excerpt":"Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availability and domain-specific concerns introduce difficulties, while obtaining human predictions for training and evaluation is costly. Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI tea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.13218","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.13218/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":"2312.13218","created_at":"2026-07-05T07:26:33.574563+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.13218v1","created_at":"2026-07-05T07:26:33.574563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.13218","created_at":"2026-07-05T07:26:33.574563+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZG3ATS37K2A","created_at":"2026-07-05T07:26:33.574563+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZG3ATS37K2ABTKG","created_at":"2026-07-05T07:26:33.574563+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZG3ATS3","created_at":"2026-07-05T07:26:33.574563+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC","json":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC.json","graph_json":"https://pith.science/api/pith-number/3ZG3ATS37K2ABTKGKX4FTFR5ZC/graph.json","events_json":"https://pith.science/api/pith-number/3ZG3ATS37K2ABTKGKX4FTFR5ZC/events.json","paper":"https://pith.science/paper/3ZG3ATS3"},"agent_actions":{"view_html":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC","download_json":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC.json","view_paper":"https://pith.science/paper/3ZG3ATS3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.13218&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZG3ATS37K2ABTKGKX4FTFR5ZC/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZG3ATS37K2ABTKGKX4FTFR5ZC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC/action/storage_attestation","attest_author":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC/action/author_attestation","sign_citation":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC/action/citation_signature","submit_replication":"https://pith.science/pith/3ZG3ATS37K2ABTKGKX4FTFR5ZC/action/replication_record"}},"created_at":"2026-07-05T07:26:33.574563+00:00","updated_at":"2026-07-05T07:26:33.574563+00:00"}