{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GNYZJ4E2G54OV7JCOB57VH6OY2","short_pith_number":"pith:GNYZJ4E2","canonical_record":{"source":{"id":"1908.06361","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-18T01:28:45Z","cross_cats_sorted":[],"title_canon_sha256":"889f42b551a8d2901cca6893b381d887f37137b526e33f9cc6587772b409df58","abstract_canon_sha256":"18e032cda93dd941fdca6908ba773a12a2d87c5ab56fe6ef0d610530978afc72"},"schema_version":"1.0"},"canonical_sha256":"337194f09a3778eafd22707bfa9fcec6af9253653900b3ca12e38779332532d2","source":{"kind":"arxiv","id":"1908.06361","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06361","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06361v1","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06361","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"pith_short_12","alias_value":"GNYZJ4E2G54O","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"pith_short_16","alias_value":"GNYZJ4E2G54OV7JC","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"pith_short_8","alias_value":"GNYZJ4E2","created_at":"2026-07-04T23:58:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GNYZJ4E2G54OV7JCOB57VH6OY2","target":"record","payload":{"canonical_record":{"source":{"id":"1908.06361","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-18T01:28:45Z","cross_cats_sorted":[],"title_canon_sha256":"889f42b551a8d2901cca6893b381d887f37137b526e33f9cc6587772b409df58","abstract_canon_sha256":"18e032cda93dd941fdca6908ba773a12a2d87c5ab56fe6ef0d610530978afc72"},"schema_version":"1.0"},"canonical_sha256":"337194f09a3778eafd22707bfa9fcec6af9253653900b3ca12e38779332532d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:15.002074Z","signature_b64":"V0fn5zdKt60j7BiEDK0Npb/YXxWkNb5OMnveRamiefAxXugfKsCuXBxfZJc9f8PFqdh6EUhHomXdKPekYRPlCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"337194f09a3778eafd22707bfa9fcec6af9253653900b3ca12e38779332532d2","last_reissued_at":"2026-07-04T23:58:15.001706Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:15.001706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.06361","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:58:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EvcBEXCdEweHjmjWTrNptHZDr7ZNoi56xgPumz+DMSdLF8MHfSQoPcYv8akuJxvZ5UiLBRXWTZZx91vrCFZqDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:52:50.198946Z"},"content_sha256":"7c9cda6e8844a519cc7522af23b8a42725faf83ce7961ea0f0efc371090190dc","schema_version":"1.0","event_id":"sha256:7c9cda6e8844a519cc7522af23b8a42725faf83ce7961ea0f0efc371090190dc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GNYZJ4E2G54OV7JCOB57VH6OY2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding Undesirable Word Embedding Associations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"David Duvenaud, Graeme Hirst, Kawin Ethayarajh","submitted_at":"2019-08-18T01:28:45Z","abstract_excerpt":"Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effecti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06361","kind":"arxiv","version":1},"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/1908.06361/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:58:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+DhlNFpW5YpImVMC0TIONtvYeq+kmConjZ3TbwKVCKirhZxLuLlvneAOoOtYN1/EQFGnnfzwkKYDTHjuYLNUBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:52:50.199916Z"},"content_sha256":"3acba8981b82e6d69c3caa8185ec4104d50913607342ebd66dd8988bd9ff0df4","schema_version":"1.0","event_id":"sha256:3acba8981b82e6d69c3caa8185ec4104d50913607342ebd66dd8988bd9ff0df4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GNYZJ4E2G54OV7JCOB57VH6OY2/bundle.json","state_url":"https://pith.science/pith/GNYZJ4E2G54OV7JCOB57VH6OY2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GNYZJ4E2G54OV7JCOB57VH6OY2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T08:52:50Z","links":{"resolver":"https://pith.science/pith/GNYZJ4E2G54OV7JCOB57VH6OY2","bundle":"https://pith.science/pith/GNYZJ4E2G54OV7JCOB57VH6OY2/bundle.json","state":"https://pith.science/pith/GNYZJ4E2G54OV7JCOB57VH6OY2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GNYZJ4E2G54OV7JCOB57VH6OY2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GNYZJ4E2G54OV7JCOB57VH6OY2","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":"18e032cda93dd941fdca6908ba773a12a2d87c5ab56fe6ef0d610530978afc72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-18T01:28:45Z","title_canon_sha256":"889f42b551a8d2901cca6893b381d887f37137b526e33f9cc6587772b409df58"},"schema_version":"1.0","source":{"id":"1908.06361","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06361","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06361v1","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06361","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"pith_short_12","alias_value":"GNYZJ4E2G54O","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"pith_short_16","alias_value":"GNYZJ4E2G54OV7JC","created_at":"2026-07-04T23:58:15Z"},{"alias_kind":"pith_short_8","alias_value":"GNYZJ4E2","created_at":"2026-07-04T23:58:15Z"}],"graph_snapshots":[{"event_id":"sha256:3acba8981b82e6d69c3caa8185ec4104d50913607342ebd66dd8988bd9ff0df4","target":"graph","created_at":"2026-07-04T23:58:15Z","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/1908.06361/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effecti","authors_text":"David Duvenaud, Graeme Hirst, Kawin Ethayarajh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-18T01:28:45Z","title":"Understanding Undesirable Word Embedding Associations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06361","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":"sha256:7c9cda6e8844a519cc7522af23b8a42725faf83ce7961ea0f0efc371090190dc","target":"record","created_at":"2026-07-04T23:58:15Z","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":"18e032cda93dd941fdca6908ba773a12a2d87c5ab56fe6ef0d610530978afc72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-18T01:28:45Z","title_canon_sha256":"889f42b551a8d2901cca6893b381d887f37137b526e33f9cc6587772b409df58"},"schema_version":"1.0","source":{"id":"1908.06361","kind":"arxiv","version":1}},"canonical_sha256":"337194f09a3778eafd22707bfa9fcec6af9253653900b3ca12e38779332532d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"337194f09a3778eafd22707bfa9fcec6af9253653900b3ca12e38779332532d2","first_computed_at":"2026-07-04T23:58:15.001706Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:58:15.001706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V0fn5zdKt60j7BiEDK0Npb/YXxWkNb5OMnveRamiefAxXugfKsCuXBxfZJc9f8PFqdh6EUhHomXdKPekYRPlCw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:58:15.002074Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.06361","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c9cda6e8844a519cc7522af23b8a42725faf83ce7961ea0f0efc371090190dc","sha256:3acba8981b82e6d69c3caa8185ec4104d50913607342ebd66dd8988bd9ff0df4"],"state_sha256":"077a294b1e2bb08c4e98618bb37d7c3b69b70048e3a678523dcb08833c6de7c1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DxIGBsduUv5OHxhYAu2nYSHQjgMm0DK7QrNVgmm5XlV0ixyRgMO9Tq0Fdh8VKWClcbxxfud6m+P+5y2Tx1q8AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T08:52:50.207321Z","bundle_sha256":"cc3605de82ce78658669a2550ccaf140fc3180db04288026a83f9cc05253069c"}}