{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3XM6DAGTBGRLIQGC4JMXRAUVBQ","short_pith_number":"pith:3XM6DAGT","schema_version":"1.0","canonical_sha256":"ddd9e180d309a2b440c2e2597882950c0229aaf2e1dd56f5a5abd685a3f9350f","source":{"kind":"arxiv","id":"1903.03862","version":2},"attestation_state":"computed","paper":{"title":"Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hila Gonen, Yoav Goldberg","submitted_at":"2019-03-09T19:56:47Z","abstract_excerpt":"Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly h"},"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":"1903.03862","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-03-09T19:56:47Z","cross_cats_sorted":[],"title_canon_sha256":"104f5af95219c3e16cb333a299213fdbd573f3d8bbd3af5263c938e9ac05fa1c","abstract_canon_sha256":"f7467e174f53a0db1cb81bd5b2554c980814f8eaa4c0ed34632c7aa95e860fbe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:06:36.913783Z","signature_b64":"7ldUzytSO1k9xAGuR1zKkHpMyy5Qehlie/ZFaG2ILnCdzTIxCqp3lHx1geadeMrxVZp7Inex3fcm6a8g5cL6Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddd9e180d309a2b440c2e2597882950c0229aaf2e1dd56f5a5abd685a3f9350f","last_reissued_at":"2026-07-05T00:06:36.913357Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:06:36.913357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hila Gonen, Yoav Goldberg","submitted_at":"2019-03-09T19:56:47Z","abstract_excerpt":"Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.03862","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/1903.03862/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":"1903.03862","created_at":"2026-07-05T00:06:36.913416+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.03862v2","created_at":"2026-07-05T00:06:36.913416+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.03862","created_at":"2026-07-05T00:06:36.913416+00:00"},{"alias_kind":"pith_short_12","alias_value":"3XM6DAGTBGRL","created_at":"2026-07-05T00:06:36.913416+00:00"},{"alias_kind":"pith_short_16","alias_value":"3XM6DAGTBGRLIQGC","created_at":"2026-07-05T00:06:36.913416+00:00"},{"alias_kind":"pith_short_8","alias_value":"3XM6DAGT","created_at":"2026-07-05T00:06:36.913416+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1906.10256","citing_title":"Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16516","citing_title":"Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2005.14165","citing_title":"Language Models are Few-Shot Learners","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ","json":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ.json","graph_json":"https://pith.science/api/pith-number/3XM6DAGTBGRLIQGC4JMXRAUVBQ/graph.json","events_json":"https://pith.science/api/pith-number/3XM6DAGTBGRLIQGC4JMXRAUVBQ/events.json","paper":"https://pith.science/paper/3XM6DAGT"},"agent_actions":{"view_html":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ","download_json":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ.json","view_paper":"https://pith.science/paper/3XM6DAGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.03862&json=true","fetch_graph":"https://pith.science/api/pith-number/3XM6DAGTBGRLIQGC4JMXRAUVBQ/graph.json","fetch_events":"https://pith.science/api/pith-number/3XM6DAGTBGRLIQGC4JMXRAUVBQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ/action/storage_attestation","attest_author":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ/action/author_attestation","sign_citation":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ/action/citation_signature","submit_replication":"https://pith.science/pith/3XM6DAGTBGRLIQGC4JMXRAUVBQ/action/replication_record"}},"created_at":"2026-07-05T00:06:36.913416+00:00","updated_at":"2026-07-05T00:06:36.913416+00:00"}