{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HMCBWAB56FMQO34LZW7FX2C33D","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":"eb98ab6523d81e2e19c451f44b210dd8c9b831d3f91948095698822960aa5ba2","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-12T20:03:08Z","title_canon_sha256":"cc17288c0319767a8a4c7528e5095aa6fa2ec6a981efd006325c493d31b2d0e8"},"schema_version":"1.0","source":{"id":"2207.05811","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.05811","created_at":"2026-07-05T04:39:51Z"},{"alias_kind":"arxiv_version","alias_value":"2207.05811v1","created_at":"2026-07-05T04:39:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.05811","created_at":"2026-07-05T04:39:51Z"},{"alias_kind":"pith_short_12","alias_value":"HMCBWAB56FMQ","created_at":"2026-07-05T04:39:51Z"},{"alias_kind":"pith_short_16","alias_value":"HMCBWAB56FMQO34L","created_at":"2026-07-05T04:39:51Z"},{"alias_kind":"pith_short_8","alias_value":"HMCBWAB5","created_at":"2026-07-05T04:39:51Z"}],"graph_snapshots":[{"event_id":"sha256:b528d2c4336d8e6b4f58ee7e262631f5a976f796ec36af0adadcea764b0a9152","target":"graph","created_at":"2026-07-05T04:39:51Z","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/2207.05811/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical diagnosis. Although there are effective methods to train fair models from scratch, how to automatically reveal and explain the unfairness of a trained model remains a challenging task. Revealing unfairness of machine learning models in interpretable fashion is a critical step towards fair and trustworthy AI. In this paper, we systematically tackle the novel task of revealing unfair models by mining interpretable evi","authors_text":"Gursimran Singh, Jian Pei, Lanjun Wang, Lingyang Chu, Mohit Bajaj, Vittorio Romaniello, Yong Zhang, Zirui Zhou","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-12T20:03:08Z","title":"Revealing Unfair Models by Mining Interpretable Evidence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.05811","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":"sha256:7f3059524f0bfe5c13f48c3d71998f0bda4363b95571e40bbbbc8d54bce670f0","target":"record","created_at":"2026-07-05T04:39:51Z","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":"eb98ab6523d81e2e19c451f44b210dd8c9b831d3f91948095698822960aa5ba2","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-12T20:03:08Z","title_canon_sha256":"cc17288c0319767a8a4c7528e5095aa6fa2ec6a981efd006325c493d31b2d0e8"},"schema_version":"1.0","source":{"id":"2207.05811","kind":"arxiv","version":1}},"canonical_sha256":"3b041b003df159076f8bcdbe5be85bd8c3f679b0ffef717c1924877d5f2ce61a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3b041b003df159076f8bcdbe5be85bd8c3f679b0ffef717c1924877d5f2ce61a","first_computed_at":"2026-07-05T04:39:51.740279Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:39:51.740279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WVAQVKMu9+Dr4BwJyMWa0Khp115snbtLWkeVwZWHoSwOO6jqLflIJlpeIRtsSN535x8dxiSBIRWdX9K/NCTNBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:39:51.740689Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.05811","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f3059524f0bfe5c13f48c3d71998f0bda4363b95571e40bbbbc8d54bce670f0","sha256:b528d2c4336d8e6b4f58ee7e262631f5a976f796ec36af0adadcea764b0a9152"],"state_sha256":"66e37e735f452a8b2c9a66cae844cb1453f6a2eb88926a5be1b941dc8e4786bd"}