{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5SPYBK4DMPDMPKOEBFFJFVQV6S","short_pith_number":"pith:5SPYBK4D","schema_version":"1.0","canonical_sha256":"ec9f80ab8363c6c7a9c4094a92d615f48ba1c92ab3178397a94c980f931cd6ca","source":{"kind":"arxiv","id":"2606.31704","version":1},"attestation_state":"computed","paper":{"title":"WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Beno\\^it Ronval, F\\'elicien Schiltz, Maxime Moussi, Siegfried Nijssen","submitted_at":"2026-06-30T14:10:42Z","abstract_excerpt":"The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups. A key obstacle to studying and mitigating such biases is the lack of face detection datasets with sensitive feature annotations. To address this gap, we introduce WIDER-FAIR, a new dataset built on the widely used WIDER-FACE benchmark, manually annotated with the perceived ethnicity and sex of each face. The dataset contains 16,256 images annotated across four ethnic groups: Asian, Black, Indian, and White, and t"},"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":"2606.31704","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:10:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a3d73f8426a49186c6f484cc26c2e1bfc15033bc0c0a3f2c965257c4a4170c4a","abstract_canon_sha256":"f932286729bf815e71e59ca0a9829a8a659865dc88f0215f0b72d5f1db185e55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:18:12.276427Z","signature_b64":"gMQ3B761FNveAZ+FdcvkUNQPLFD6i73E7q5Zz/8l02SdBrwefOJJz6nt/XOl2yBiPlJzGudOKtHjT57Lan4lCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec9f80ab8363c6c7a9c4094a92d615f48ba1c92ab3178397a94c980f931cd6ca","last_reissued_at":"2026-07-01T01:18:12.275950Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:18:12.275950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Beno\\^it Ronval, F\\'elicien Schiltz, Maxime Moussi, Siegfried Nijssen","submitted_at":"2026-06-30T14:10:42Z","abstract_excerpt":"The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups. A key obstacle to studying and mitigating such biases is the lack of face detection datasets with sensitive feature annotations. To address this gap, we introduce WIDER-FAIR, a new dataset built on the widely used WIDER-FACE benchmark, manually annotated with the perceived ethnicity and sex of each face. The dataset contains 16,256 images annotated across four ethnic groups: Asian, Black, Indian, and White, and t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31704","kind":"arxiv","version":1},"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/2606.31704/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":"2606.31704","created_at":"2026-07-01T01:18:12.276013+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.31704v1","created_at":"2026-07-01T01:18:12.276013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31704","created_at":"2026-07-01T01:18:12.276013+00:00"},{"alias_kind":"pith_short_12","alias_value":"5SPYBK4DMPDM","created_at":"2026-07-01T01:18:12.276013+00:00"},{"alias_kind":"pith_short_16","alias_value":"5SPYBK4DMPDMPKOE","created_at":"2026-07-01T01:18:12.276013+00:00"},{"alias_kind":"pith_short_8","alias_value":"5SPYBK4D","created_at":"2026-07-01T01:18:12.276013+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/5SPYBK4DMPDMPKOEBFFJFVQV6S","json":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S.json","graph_json":"https://pith.science/api/pith-number/5SPYBK4DMPDMPKOEBFFJFVQV6S/graph.json","events_json":"https://pith.science/api/pith-number/5SPYBK4DMPDMPKOEBFFJFVQV6S/events.json","paper":"https://pith.science/paper/5SPYBK4D"},"agent_actions":{"view_html":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S","download_json":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S.json","view_paper":"https://pith.science/paper/5SPYBK4D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.31704&json=true","fetch_graph":"https://pith.science/api/pith-number/5SPYBK4DMPDMPKOEBFFJFVQV6S/graph.json","fetch_events":"https://pith.science/api/pith-number/5SPYBK4DMPDMPKOEBFFJFVQV6S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S/action/storage_attestation","attest_author":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S/action/author_attestation","sign_citation":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S/action/citation_signature","submit_replication":"https://pith.science/pith/5SPYBK4DMPDMPKOEBFFJFVQV6S/action/replication_record"}},"created_at":"2026-07-01T01:18:12.276013+00:00","updated_at":"2026-07-01T01:18:12.276013+00:00"}