{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LZYXJ6II3B42ZHN4PTLEILIGEJ","short_pith_number":"pith:LZYXJ6II","schema_version":"1.0","canonical_sha256":"5e7174f908d879ac9dbc7cd6442d062262726766c9ce4b955993b2f50caa1363","source":{"kind":"arxiv","id":"2412.01585","version":3},"attestation_state":"computed","paper":{"title":"FairML: A Julia Package for Fair Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Jan Pablo Burgard, Jo\\~ao Vitor Pamplona","submitted_at":"2024-12-02T15:04:51Z","abstract_excerpt":"In this paper, we propose FairML.jl, a Julia package providing a framework for fair classification in machine learning. In this framework, the fair learning process is divided into three stages. Each stage aims to reduce unfairness, such as disparate impact and disparate mistreatment, in the final prediction. For the preprocessing stage, we present a resampling method that addresses unfairness coming from data imbalances. The in-processing phase consist of a classification method. This can be either one coming from the MLJ.jl package, or a user defined one. For this phase, we incorporate fair "},"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":"2412.01585","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-02T15:04:51Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"65376af08f6ad1d67ff4f675f192adf5fa2351d3c7b0c95bb995df5a7c897f02","abstract_canon_sha256":"7104be2a9bb67c5016e89f66a4522be2d253d3c9c63fa62e2e3d6e4455a8abf9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:59.458943Z","signature_b64":"5gayl0yubx3nfFhu9Cs9oSVIyuvMxgRIEeNWuSF4ommLxfgShrnWhJ5uSI9iR+LOtSQGiOtI2QTJGMRplorTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e7174f908d879ac9dbc7cd6442d062262726766c9ce4b955993b2f50caa1363","last_reissued_at":"2026-07-05T09:45:59.458487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:59.458487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FairML: A Julia Package for Fair Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Jan Pablo Burgard, Jo\\~ao Vitor Pamplona","submitted_at":"2024-12-02T15:04:51Z","abstract_excerpt":"In this paper, we propose FairML.jl, a Julia package providing a framework for fair classification in machine learning. In this framework, the fair learning process is divided into three stages. Each stage aims to reduce unfairness, such as disparate impact and disparate mistreatment, in the final prediction. For the preprocessing stage, we present a resampling method that addresses unfairness coming from data imbalances. The in-processing phase consist of a classification method. This can be either one coming from the MLJ.jl package, or a user defined one. For this phase, we incorporate fair "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01585","kind":"arxiv","version":3},"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/2412.01585/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":"2412.01585","created_at":"2026-07-05T09:45:59.458545+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.01585v3","created_at":"2026-07-05T09:45:59.458545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01585","created_at":"2026-07-05T09:45:59.458545+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZYXJ6II3B42","created_at":"2026-07-05T09:45:59.458545+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZYXJ6II3B42ZHN4","created_at":"2026-07-05T09:45:59.458545+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZYXJ6II","created_at":"2026-07-05T09:45:59.458545+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/LZYXJ6II3B42ZHN4PTLEILIGEJ","json":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ.json","graph_json":"https://pith.science/api/pith-number/LZYXJ6II3B42ZHN4PTLEILIGEJ/graph.json","events_json":"https://pith.science/api/pith-number/LZYXJ6II3B42ZHN4PTLEILIGEJ/events.json","paper":"https://pith.science/paper/LZYXJ6II"},"agent_actions":{"view_html":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ","download_json":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ.json","view_paper":"https://pith.science/paper/LZYXJ6II","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.01585&json=true","fetch_graph":"https://pith.science/api/pith-number/LZYXJ6II3B42ZHN4PTLEILIGEJ/graph.json","fetch_events":"https://pith.science/api/pith-number/LZYXJ6II3B42ZHN4PTLEILIGEJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ/action/storage_attestation","attest_author":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ/action/author_attestation","sign_citation":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ/action/citation_signature","submit_replication":"https://pith.science/pith/LZYXJ6II3B42ZHN4PTLEILIGEJ/action/replication_record"}},"created_at":"2026-07-05T09:45:59.458545+00:00","updated_at":"2026-07-05T09:45:59.458545+00:00"}