{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QLO5K246I5LQTVW2RTZL53FRKJ","short_pith_number":"pith:QLO5K246","schema_version":"1.0","canonical_sha256":"82ddd56b9e475709d6da8cf2beecb1526436fd5f43c4876c4a9f269b842f8169","source":{"kind":"arxiv","id":"2601.23221","version":2},"attestation_state":"computed","paper":{"title":"Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Argyris Kalogeratos, Gabriel Singer, Nicolas Vayatis, Olivier Vo Van, Samuel Gruffaz","submitted_at":"2026-01-30T17:45:32Z","abstract_excerpt":"As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort. Aggregating subjective labels, though, may amplify individual biases, particularly regarding sensitive features, raising fairness concerns. Nonetheless, fairness in crowdsourced aggregation remains largely unexplored, with no existing convergence guarantees and only limited post-processing approaches for enforcing $\\varepsilon$-fairness under demographic parity. We address this gap by analyzing the fairness s of crowdsourced aggregation met"},"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":"2601.23221","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-30T17:45:32Z","cross_cats_sorted":[],"title_canon_sha256":"260fcc08501b87493880ad46ae5ec3859d853a17a5cd84151195806eb8e63257","abstract_canon_sha256":"cfd077f11c248e9c979a38fbe0690eb66eeb0ec0d706a7c811b0489ed1a337eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:46.800600Z","signature_b64":"9zIJw1nIrzEfl89wmiY8/jjTN44ySCYON/n+FhFA+iOh2W8+6HANqzR7mKlR72/s+t7obUo4i3LsSbitnzQ3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82ddd56b9e475709d6da8cf2beecb1526436fd5f43c4876c4a9f269b842f8169","last_reissued_at":"2026-06-09T01:05:46.800053Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:46.800053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Argyris Kalogeratos, Gabriel Singer, Nicolas Vayatis, Olivier Vo Van, Samuel Gruffaz","submitted_at":"2026-01-30T17:45:32Z","abstract_excerpt":"As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort. Aggregating subjective labels, though, may amplify individual biases, particularly regarding sensitive features, raising fairness concerns. Nonetheless, fairness in crowdsourced aggregation remains largely unexplored, with no existing convergence guarantees and only limited post-processing approaches for enforcing $\\varepsilon$-fairness under demographic parity. We address this gap by analyzing the fairness s of crowdsourced aggregation met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.23221","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/2601.23221/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":"2601.23221","created_at":"2026-06-09T01:05:46.800120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.23221v2","created_at":"2026-06-09T01:05:46.800120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.23221","created_at":"2026-06-09T01:05:46.800120+00:00"},{"alias_kind":"pith_short_12","alias_value":"QLO5K246I5LQ","created_at":"2026-06-09T01:05:46.800120+00:00"},{"alias_kind":"pith_short_16","alias_value":"QLO5K246I5LQTVW2","created_at":"2026-06-09T01:05:46.800120+00:00"},{"alias_kind":"pith_short_8","alias_value":"QLO5K246","created_at":"2026-06-09T01:05:46.800120+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/QLO5K246I5LQTVW2RTZL53FRKJ","json":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ.json","graph_json":"https://pith.science/api/pith-number/QLO5K246I5LQTVW2RTZL53FRKJ/graph.json","events_json":"https://pith.science/api/pith-number/QLO5K246I5LQTVW2RTZL53FRKJ/events.json","paper":"https://pith.science/paper/QLO5K246"},"agent_actions":{"view_html":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ","download_json":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ.json","view_paper":"https://pith.science/paper/QLO5K246","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.23221&json=true","fetch_graph":"https://pith.science/api/pith-number/QLO5K246I5LQTVW2RTZL53FRKJ/graph.json","fetch_events":"https://pith.science/api/pith-number/QLO5K246I5LQTVW2RTZL53FRKJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ/action/storage_attestation","attest_author":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ/action/author_attestation","sign_citation":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ/action/citation_signature","submit_replication":"https://pith.science/pith/QLO5K246I5LQTVW2RTZL53FRKJ/action/replication_record"}},"created_at":"2026-06-09T01:05:46.800120+00:00","updated_at":"2026-06-09T01:05:46.800120+00:00"}