{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CKPBAKLKS6HJ673CVFOY26TRSK","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":"e626d9b9fad4ae15ae3e8f78adf2666aa4fe4ac3ffa1dcb447083ba4bc404fde","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-11T16:22:37Z","title_canon_sha256":"3e78d666b4c596e5c5b3e719d6395d78479d3652a957310a18bf1656bc8cd40e"},"schema_version":"1.0","source":{"id":"2312.06499","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.06499","created_at":"2026-07-05T09:21:01Z"},{"alias_kind":"arxiv_version","alias_value":"2312.06499v4","created_at":"2026-07-05T09:21:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.06499","created_at":"2026-07-05T09:21:01Z"},{"alias_kind":"pith_short_12","alias_value":"CKPBAKLKS6HJ","created_at":"2026-07-05T09:21:01Z"},{"alias_kind":"pith_short_16","alias_value":"CKPBAKLKS6HJ673C","created_at":"2026-07-05T09:21:01Z"},{"alias_kind":"pith_short_8","alias_value":"CKPBAKLK","created_at":"2026-07-05T09:21:01Z"}],"graph_snapshots":[{"event_id":"sha256:ffdf2b873d83da21f1d3c71539e707f4e543aff10e5a9982de2f8f6256d5a48e","target":"graph","created_at":"2026-07-05T09:21:01Z","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/2312.06499/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ensuring fairness in NLP models is crucial, as they often encode sensitive attributes like gender and ethnicity, leading to biased outcomes. Current concept erasure methods attempt to mitigate this by modifying final latent representations to remove sensitive information without retraining the entire model. However, these methods typically rely on linear classifiers, which leave models vulnerable to non-linear adversaries capable of recovering sensitive information.\n  We introduce Targeted Concept Erasure (TaCo), a novel approach that removes sensitive information from final latent representat","authors_text":"Agustin Picard, Fanny Jourdan, Laurent Risser, Louis B\\'ethune, Nicholas Asher","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-11T16:22:37Z","title":"TaCo: Targeted Concept Erasure Prevents Non-Linear Classifiers From Detecting Protected Attributes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.06499","kind":"arxiv","version":4},"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:d086d64f585408261260f52cc8451584cd52b611a1b271ac1585e04915055800","target":"record","created_at":"2026-07-05T09:21:01Z","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":"e626d9b9fad4ae15ae3e8f78adf2666aa4fe4ac3ffa1dcb447083ba4bc404fde","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-11T16:22:37Z","title_canon_sha256":"3e78d666b4c596e5c5b3e719d6395d78479d3652a957310a18bf1656bc8cd40e"},"schema_version":"1.0","source":{"id":"2312.06499","kind":"arxiv","version":4}},"canonical_sha256":"129e10296a978e9f7f62a95d8d7a7192a5ab14286f965ae9887a916679015eec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"129e10296a978e9f7f62a95d8d7a7192a5ab14286f965ae9887a916679015eec","first_computed_at":"2026-07-05T09:21:01.852861Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:21:01.852861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bRQY8AqwBEtQx/EuAeSgc9c+AGxQC6IfizCtQIJ30o//UaIqtPoIQZdJmkSnJkfuOA8dpO5y8/hjQSmKK4L1Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:21:01.853428Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.06499","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d086d64f585408261260f52cc8451584cd52b611a1b271ac1585e04915055800","sha256:ffdf2b873d83da21f1d3c71539e707f4e543aff10e5a9982de2f8f6256d5a48e"],"state_sha256":"3cdad1ce3031d8dc32688cf3aaef6281909bfd7092e83498dc298644ab5fe045"}