{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VYRGNBC3SEUNCTHIAHQIURP5JY","short_pith_number":"pith:VYRGNBC3","schema_version":"1.0","canonical_sha256":"ae2266845b9128d14ce801e08a45fd4e3bd6287c00677124dbe3894650a3b9e9","source":{"kind":"arxiv","id":"2108.07403","version":2},"attestation_state":"computed","paper":{"title":"FARF: A Fair and Adaptive Random Forests Classifier","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Albert Bifet, Jeremy C. Weiss, Wenbin Zhang, Wolfgang Nejdl, Xiangliang Zhang","submitted_at":"2021-08-17T02:06:54Z","abstract_excerpt":"As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyperparameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemb"},"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":"2108.07403","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-17T02:06:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b2aa79eb16b087a73803b1333c63ec7e95a3c946ab106d837e5a9d13131310ca","abstract_canon_sha256":"5560ad21bdf689d71266ae1981844652b560fa61b22fc9052d1ee683b5083230"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:52.331605Z","signature_b64":"1CMYXeA1zpN2Fecly0FPAv+WN/Ne7Yq9utcR/q9YiXVM00D433SqbLT5x/84zwyS4yaa9OZaczGhvfg+suGzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae2266845b9128d14ce801e08a45fd4e3bd6287c00677124dbe3894650a3b9e9","last_reissued_at":"2026-07-05T03:07:52.331049Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:52.331049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FARF: A Fair and Adaptive Random Forests Classifier","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Albert Bifet, Jeremy C. Weiss, Wenbin Zhang, Wolfgang Nejdl, Xiangliang Zhang","submitted_at":"2021-08-17T02:06:54Z","abstract_excerpt":"As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyperparameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.07403","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.07403/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":"2108.07403","created_at":"2026-07-05T03:07:52.331104+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.07403v2","created_at":"2026-07-05T03:07:52.331104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.07403","created_at":"2026-07-05T03:07:52.331104+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYRGNBC3SEUN","created_at":"2026-07-05T03:07:52.331104+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYRGNBC3SEUNCTHI","created_at":"2026-07-05T03:07:52.331104+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYRGNBC3","created_at":"2026-07-05T03:07:52.331104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.01585","citing_title":"FairML: A Julia Package for Fair Classification","ref_index":970,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY","json":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY.json","graph_json":"https://pith.science/api/pith-number/VYRGNBC3SEUNCTHIAHQIURP5JY/graph.json","events_json":"https://pith.science/api/pith-number/VYRGNBC3SEUNCTHIAHQIURP5JY/events.json","paper":"https://pith.science/paper/VYRGNBC3"},"agent_actions":{"view_html":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY","download_json":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY.json","view_paper":"https://pith.science/paper/VYRGNBC3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.07403&json=true","fetch_graph":"https://pith.science/api/pith-number/VYRGNBC3SEUNCTHIAHQIURP5JY/graph.json","fetch_events":"https://pith.science/api/pith-number/VYRGNBC3SEUNCTHIAHQIURP5JY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY/action/storage_attestation","attest_author":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY/action/author_attestation","sign_citation":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY/action/citation_signature","submit_replication":"https://pith.science/pith/VYRGNBC3SEUNCTHIAHQIURP5JY/action/replication_record"}},"created_at":"2026-07-05T03:07:52.331104+00:00","updated_at":"2026-07-05T03:07:52.331104+00:00"}