{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EWUGNPFQ3ALPE3YEPNEVOCV3UA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4365b848f064a9c9ccdd7164110254fcfaff1358b586287ac783d48e3fe4c8ce","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-05T21:54:29Z","title_canon_sha256":"6e11213d7bad1408b45ac229afc1d496d977aabcec534852fa31b8159a131b08"},"schema_version":"1.0","source":{"id":"2212.02614","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.02614","created_at":"2026-07-05T05:22:32Z"},{"alias_kind":"arxiv_version","alias_value":"2212.02614v1","created_at":"2026-07-05T05:22:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.02614","created_at":"2026-07-05T05:22:32Z"},{"alias_kind":"pith_short_12","alias_value":"EWUGNPFQ3ALP","created_at":"2026-07-05T05:22:32Z"},{"alias_kind":"pith_short_16","alias_value":"EWUGNPFQ3ALPE3YE","created_at":"2026-07-05T05:22:32Z"},{"alias_kind":"pith_short_8","alias_value":"EWUGNPFQ","created_at":"2026-07-05T05:22:32Z"}],"graph_snapshots":[{"event_id":"sha256:413cab2785faf1e3112eaeebd96be4c1b34fa7ba5eb62d25621c23a6c946b4ec","target":"graph","created_at":"2026-07-05T05:22:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.02614/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. In this work, we evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensembl","authors_text":"Amanda Kolopanis, Antonio Collante, Diego Elias Costa, Emad Shihab, Foutse Khomh, Khaled Badran, Pierre-Olivier C\\^ot\\'e, Rached Bouchoucha","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-05T21:54:29Z","title":"Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.02614","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:844737eecd09dd3125b944ce0f0b4ebf07deb5c8f713427b0febf2541d8131ad","target":"record","created_at":"2026-07-05T05:22:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4365b848f064a9c9ccdd7164110254fcfaff1358b586287ac783d48e3fe4c8ce","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-05T21:54:29Z","title_canon_sha256":"6e11213d7bad1408b45ac229afc1d496d977aabcec534852fa31b8159a131b08"},"schema_version":"1.0","source":{"id":"2212.02614","kind":"arxiv","version":1}},"canonical_sha256":"25a866bcb0d816f26f047b49570abba0065f140fff27996c652f19221fd04510","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25a866bcb0d816f26f047b49570abba0065f140fff27996c652f19221fd04510","first_computed_at":"2026-07-05T05:22:32.743636Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:22:32.743636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YepaPP34q8+C79yJi5jU6TAwx1cfbUQS/MSA7Mx7ZwIVIvUbPIcLN/IfS9tEsf+KirDO9P2dw7mAlrr95duTAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:22:32.744048Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.02614","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:844737eecd09dd3125b944ce0f0b4ebf07deb5c8f713427b0febf2541d8131ad","sha256:413cab2785faf1e3112eaeebd96be4c1b34fa7ba5eb62d25621c23a6c946b4ec"],"state_sha256":"baea86377fe0fa8b28add55448ecbbb39f79c6c5bab3d323634ab72d9d4dae3f"}