{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FOSXDNGCDBSM2X4NST5KT7GI4H","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":"85f79cd62508c82c626f49194b7763c2361c82786e4199d9236de0fb4ee6a3d9","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T02:57:34Z","title_canon_sha256":"9f137c45430ab4b86a3913a624cb4865e7b7f2847807e7c4d120c4004ad6c8aa"},"schema_version":"1.0","source":{"id":"2203.02110","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.02110","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"arxiv_version","alias_value":"2203.02110v1","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02110","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_12","alias_value":"FOSXDNGCDBSM","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_16","alias_value":"FOSXDNGCDBSM2X4N","created_at":"2026-07-05T04:02:01Z"},{"alias_kind":"pith_short_8","alias_value":"FOSXDNGC","created_at":"2026-07-05T04:02:01Z"}],"graph_snapshots":[{"event_id":"sha256:c4512d16d81acfdb49f3f2c4f292ad1e10c3b0da737339d78a4607613566c4cb","target":"graph","created_at":"2026-07-05T04:02:01Z","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/2203.02110/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many works have shown that deep learning-based medical image classification models can exhibit bias toward certain demographic attributes like race, gender, and age. Existing bias mitigation methods primarily focus on learning debiased models, which may not necessarily guarantee all sensitive information can be removed and usually comes with considerable accuracy degradation on both privileged and unprivileged groups. To tackle this issue, we propose a method, FairPrune, that achieves fairness by pruning. Conventionally, pruning is used to reduce the model size for efficient inference. However","authors_text":"Dewen Zeng, Jingtong Hu, Xiaowei Xu, Yawen Wu, Yiyu Shi","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T02:57:34Z","title":"FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02110","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:adbba5ff6d89e1c2ea2941d13b18eafb690caa361b2e6469bcbe0a2e843b1e71","target":"record","created_at":"2026-07-05T04:02:01Z","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":"85f79cd62508c82c626f49194b7763c2361c82786e4199d9236de0fb4ee6a3d9","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-03-04T02:57:34Z","title_canon_sha256":"9f137c45430ab4b86a3913a624cb4865e7b7f2847807e7c4d120c4004ad6c8aa"},"schema_version":"1.0","source":{"id":"2203.02110","kind":"arxiv","version":1}},"canonical_sha256":"2ba571b4c21864cd5f8d94faa9fcc8e1d1cc4a198eeb5e1195ebe916ce677e79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ba571b4c21864cd5f8d94faa9fcc8e1d1cc4a198eeb5e1195ebe916ce677e79","first_computed_at":"2026-07-05T04:02:01.110178Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:02:01.110178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"78LjwSjLolL6D0Q1huekMsOsF3pgbi8p7wENX235rH6Jugv6o3rS61TxukfyGBhN2Sy4afi77P1O8FVunDjyBw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:02:01.110654Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.02110","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:adbba5ff6d89e1c2ea2941d13b18eafb690caa361b2e6469bcbe0a2e843b1e71","sha256:c4512d16d81acfdb49f3f2c4f292ad1e10c3b0da737339d78a4607613566c4cb"],"state_sha256":"a145340f139053b58d7f8ff0ead7dcb10baed9ed7b903a6f390cee230b96ada3"}