{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:L2OT6ZSTYWZMEEXNC7SOVWYO35","short_pith_number":"pith:L2OT6ZST","schema_version":"1.0","canonical_sha256":"5e9d3f6653c5b2c212ed17e4eadb0edf475f14e14de64344d955559853399c6c","source":{"kind":"arxiv","id":"2111.08856","version":2},"attestation_state":"computed","paper":{"title":"Fairness Testing of Deep Image Classification with Adequacy Metrics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingyi Wang, Jun Sun, Peixin Zhang, Xinyu Wang","submitted_at":"2021-11-17T01:30:13Z","abstract_excerpt":"As deep image classification applications, e.g., face recognition, become increasingly prevalent in our daily lives, their fairness issues raise more and more concern. It is thus crucial to comprehensively test the fairness of these applications before deployment. Existing fairness testing methods suffer from the following limitations: 1) applicability, i.e., they are only applicable for structured data or text without handling the high-dimensional and abstract domain sampling in the semantic level for image classification applications; 2) functionality, i.e., they generate unfair samples with"},"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":"2111.08856","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-17T01:30:13Z","cross_cats_sorted":[],"title_canon_sha256":"c4e016488ca9cbbdc67fc8d72131aa4ac56892b540f1aaa73c58b9b36205c1e1","abstract_canon_sha256":"a3f6e64a5ac722a9b13152f5d4750242952b2fe0b63372c95ec8bd2d34c0ec2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:36:58.590575Z","signature_b64":"yYxGHDJOXnIBh9Ee86/5DC+8kCIMJf9jo1Qqz7DS6jdE3IZCuuehZoxOJGNwCxemnFHta6XaqVYAe9N9JXz0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e9d3f6653c5b2c212ed17e4eadb0edf475f14e14de64344d955559853399c6c","last_reissued_at":"2026-07-05T03:36:58.590018Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:36:58.590018Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fairness Testing of Deep Image Classification with Adequacy Metrics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingyi Wang, Jun Sun, Peixin Zhang, Xinyu Wang","submitted_at":"2021-11-17T01:30:13Z","abstract_excerpt":"As deep image classification applications, e.g., face recognition, become increasingly prevalent in our daily lives, their fairness issues raise more and more concern. It is thus crucial to comprehensively test the fairness of these applications before deployment. Existing fairness testing methods suffer from the following limitations: 1) applicability, i.e., they are only applicable for structured data or text without handling the high-dimensional and abstract domain sampling in the semantic level for image classification applications; 2) functionality, i.e., they generate unfair samples with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.08856","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/2111.08856/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":"2111.08856","created_at":"2026-07-05T03:36:58.590073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.08856v2","created_at":"2026-07-05T03:36:58.590073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.08856","created_at":"2026-07-05T03:36:58.590073+00:00"},{"alias_kind":"pith_short_12","alias_value":"L2OT6ZSTYWZM","created_at":"2026-07-05T03:36:58.590073+00:00"},{"alias_kind":"pith_short_16","alias_value":"L2OT6ZSTYWZMEEXN","created_at":"2026-07-05T03:36:58.590073+00:00"},{"alias_kind":"pith_short_8","alias_value":"L2OT6ZST","created_at":"2026-07-05T03:36:58.590073+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15775","citing_title":"Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?","ref_index":88,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35","json":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35.json","graph_json":"https://pith.science/api/pith-number/L2OT6ZSTYWZMEEXNC7SOVWYO35/graph.json","events_json":"https://pith.science/api/pith-number/L2OT6ZSTYWZMEEXNC7SOVWYO35/events.json","paper":"https://pith.science/paper/L2OT6ZST"},"agent_actions":{"view_html":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35","download_json":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35.json","view_paper":"https://pith.science/paper/L2OT6ZST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.08856&json=true","fetch_graph":"https://pith.science/api/pith-number/L2OT6ZSTYWZMEEXNC7SOVWYO35/graph.json","fetch_events":"https://pith.science/api/pith-number/L2OT6ZSTYWZMEEXNC7SOVWYO35/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35/action/storage_attestation","attest_author":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35/action/author_attestation","sign_citation":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35/action/citation_signature","submit_replication":"https://pith.science/pith/L2OT6ZSTYWZMEEXNC7SOVWYO35/action/replication_record"}},"created_at":"2026-07-05T03:36:58.590073+00:00","updated_at":"2026-07-05T03:36:58.590073+00:00"}