{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LXJEZ7LFTXHTAYIARQCEGPU4AZ","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":"a55d7fff8d415f7edb6de7cf595dc45c69171a989249f60383c58a45b8181150","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-26T12:31:10Z","title_canon_sha256":"b05bbe590797537b19d4c2187c74040bfec73a5155b58eac1a908339afd37ed8"},"schema_version":"1.0","source":{"id":"2411.17374","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17374","created_at":"2026-07-05T11:59:55Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17374v2","created_at":"2026-07-05T11:59:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17374","created_at":"2026-07-05T11:59:55Z"},{"alias_kind":"pith_short_12","alias_value":"LXJEZ7LFTXHT","created_at":"2026-07-05T11:59:55Z"},{"alias_kind":"pith_short_16","alias_value":"LXJEZ7LFTXHTAYIA","created_at":"2026-07-05T11:59:55Z"},{"alias_kind":"pith_short_8","alias_value":"LXJEZ7LF","created_at":"2026-07-05T11:59:55Z"}],"graph_snapshots":[{"event_id":"sha256:0b712b2f4d899f0a7a1fdd4d84c0da75647618bd3d4235b0738017f81cf421ad","target":"graph","created_at":"2026-07-05T11:59:55Z","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/2411.17374/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fairness in both Machine Learning (ML) predictions and human decision-making is essential, yet both are susceptible to different forms of bias, such as algorithmic and data-driven in ML, and cognitive or subjective in humans. In this study, we examine fairness using a real-world university admissions dataset comprising 870 applicant profiles, leveraging three ML models: XGB, Bi-LSTM, and KNN, alongside BERT embeddings for textual features. To evaluate individual fairness, we introduce a consistency metric that quantifies agreement in decisions among ML models and human experts with diverse bac","authors_text":"Junhua Liu, Kwan Hui Lim, Roy Ka-Wei Lee","cross_cats":["cs.AI","cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-26T12:31:10Z","title":"Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17374","kind":"arxiv","version":2},"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":"sha256:9af8ad164d3856ae8576c281bd568cd25c96df639fe06af9d4acb03067794213","target":"record","created_at":"2026-07-05T11:59:55Z","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":"a55d7fff8d415f7edb6de7cf595dc45c69171a989249f60383c58a45b8181150","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-26T12:31:10Z","title_canon_sha256":"b05bbe590797537b19d4c2187c74040bfec73a5155b58eac1a908339afd37ed8"},"schema_version":"1.0","source":{"id":"2411.17374","kind":"arxiv","version":2}},"canonical_sha256":"5dd24cfd659dcf3061008c04433e9c06646877f2dcab567dcdfef9ca5edf73cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5dd24cfd659dcf3061008c04433e9c06646877f2dcab567dcdfef9ca5edf73cf","first_computed_at":"2026-07-05T11:59:55.445817Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:59:55.445817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f85UvSqgR0e1EVmVEy1XGR2u/Xd5owjNgwESaL2+ESjSavJ2z/CI1VYGLYZAbreUyhoTh2o3ai78bFSZazz4Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:59:55.446269Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.17374","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9af8ad164d3856ae8576c281bd568cd25c96df639fe06af9d4acb03067794213","sha256:0b712b2f4d899f0a7a1fdd4d84c0da75647618bd3d4235b0738017f81cf421ad"],"state_sha256":"134361a67cbb8d688ea74f36ef4d270a210e0ee01ed445770e7c2a763755c02f"}