{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4PUKRKZC7FPX6NECHCOOZYD65Q","short_pith_number":"pith:4PUKRKZC","canonical_record":{"source":{"id":"2506.02447","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2025-06-03T05:10:42Z","cross_cats_sorted":[],"title_canon_sha256":"c64f5ec88bb06c235bca51fd08bc541b164e9e213e5d7dbec253aade62847bdc","abstract_canon_sha256":"1428259d3c89e2c386ed414d72af7a5ebada095fb5f458ac50892e355e1f5aa2"},"schema_version":"1.0"},"canonical_sha256":"e3e8a8ab22f95f7f3482389cece07eec3338a2f4f4865139836349c75dfabc0d","source":{"kind":"arxiv","id":"2506.02447","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.02447","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.02447v1","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02447","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"pith_short_12","alias_value":"4PUKRKZC7FPX","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"pith_short_16","alias_value":"4PUKRKZC7FPX6NEC","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"pith_short_8","alias_value":"4PUKRKZC","created_at":"2026-07-05T11:15:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4PUKRKZC7FPX6NECHCOOZYD65Q","target":"record","payload":{"canonical_record":{"source":{"id":"2506.02447","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2025-06-03T05:10:42Z","cross_cats_sorted":[],"title_canon_sha256":"c64f5ec88bb06c235bca51fd08bc541b164e9e213e5d7dbec253aade62847bdc","abstract_canon_sha256":"1428259d3c89e2c386ed414d72af7a5ebada095fb5f458ac50892e355e1f5aa2"},"schema_version":"1.0"},"canonical_sha256":"e3e8a8ab22f95f7f3482389cece07eec3338a2f4f4865139836349c75dfabc0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:02.115301Z","signature_b64":"Btm6Uok80Aqh/kuulStld/pIVYQB+Cl7O3h5kqm4cnlOkPMABA0zEBnsFj5g2eMROg0TDsj6Pfr6RVaulHfNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3e8a8ab22f95f7f3482389cece07eec3338a2f4f4865139836349c75dfabc0d","last_reissued_at":"2026-07-05T11:15:02.114882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:02.114882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.02447","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":"2026-07-05T11:15:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xfp7O4c7TkrzHAN0gfpyq8y235yLlwLzUGpjFa/Ws04qQdkqQQwmS86Z9sVDGe8yjZw358CHdzbZCH7qiEXgDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:36:03.106944Z"},"content_sha256":"ba845675295a66c5085e67709060363fa3225f2069afd0d907858c5a9041bd4b","schema_version":"1.0","event_id":"sha256:ba845675295a66c5085e67709060363fa3225f2069afd0d907858c5a9041bd4b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4PUKRKZC7FPX6NECHCOOZYD65Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visualization for interactively adjusting the de-bias effect of word embedding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Arisa Sugino, Takayuki Itoh","submitted_at":"2025-06-03T05:10:42Z","abstract_excerpt":"Word embedding, which converts words into numerical values, is an important natural language processing technique and widely used. One of the serious problems of word embedding is that the bias will be learned and affect the model if the dataset used for pre-training contains bias. On the other hand, indiscriminate removal of bias from word embeddings may result in the loss of information, even if the bias is undesirable to us. As a result, a risk of model performance degradation due to bias removal will be another problem. As a solution to this problem, we focus on gender bias in Japanese and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02447","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/2506.02447/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":"2026-07-05T11:15:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SOKpLp4hO/epIae7enpWANfn9MwlS6rp/VkMu7H25hQUAGRCIKrPyioE2ZZ6MKa+01olTxplZtJMmsZxfkT2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:36:03.107841Z"},"content_sha256":"2a124d407c9194c790c9d14e1cddf1eab6e32e80d034f3020e978a1b98556360","schema_version":"1.0","event_id":"sha256:2a124d407c9194c790c9d14e1cddf1eab6e32e80d034f3020e978a1b98556360"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4PUKRKZC7FPX6NECHCOOZYD65Q/bundle.json","state_url":"https://pith.science/pith/4PUKRKZC7FPX6NECHCOOZYD65Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4PUKRKZC7FPX6NECHCOOZYD65Q/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":"2026-08-07T23:36:03Z","links":{"resolver":"https://pith.science/pith/4PUKRKZC7FPX6NECHCOOZYD65Q","bundle":"https://pith.science/pith/4PUKRKZC7FPX6NECHCOOZYD65Q/bundle.json","state":"https://pith.science/pith/4PUKRKZC7FPX6NECHCOOZYD65Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4PUKRKZC7FPX6NECHCOOZYD65Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4PUKRKZC7FPX6NECHCOOZYD65Q","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":"1428259d3c89e2c386ed414d72af7a5ebada095fb5f458ac50892e355e1f5aa2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2025-06-03T05:10:42Z","title_canon_sha256":"c64f5ec88bb06c235bca51fd08bc541b164e9e213e5d7dbec253aade62847bdc"},"schema_version":"1.0","source":{"id":"2506.02447","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.02447","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.02447v1","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02447","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"pith_short_12","alias_value":"4PUKRKZC7FPX","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"pith_short_16","alias_value":"4PUKRKZC7FPX6NEC","created_at":"2026-07-05T11:15:02Z"},{"alias_kind":"pith_short_8","alias_value":"4PUKRKZC","created_at":"2026-07-05T11:15:02Z"}],"graph_snapshots":[{"event_id":"sha256:2a124d407c9194c790c9d14e1cddf1eab6e32e80d034f3020e978a1b98556360","target":"graph","created_at":"2026-07-05T11:15:02Z","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/2506.02447/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Word embedding, which converts words into numerical values, is an important natural language processing technique and widely used. One of the serious problems of word embedding is that the bias will be learned and affect the model if the dataset used for pre-training contains bias. On the other hand, indiscriminate removal of bias from word embeddings may result in the loss of information, even if the bias is undesirable to us. As a result, a risk of model performance degradation due to bias removal will be another problem. As a solution to this problem, we focus on gender bias in Japanese and","authors_text":"Arisa Sugino, Takayuki Itoh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2025-06-03T05:10:42Z","title":"Visualization for interactively adjusting the de-bias effect of word embedding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02447","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:ba845675295a66c5085e67709060363fa3225f2069afd0d907858c5a9041bd4b","target":"record","created_at":"2026-07-05T11:15:02Z","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":"1428259d3c89e2c386ed414d72af7a5ebada095fb5f458ac50892e355e1f5aa2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2025-06-03T05:10:42Z","title_canon_sha256":"c64f5ec88bb06c235bca51fd08bc541b164e9e213e5d7dbec253aade62847bdc"},"schema_version":"1.0","source":{"id":"2506.02447","kind":"arxiv","version":1}},"canonical_sha256":"e3e8a8ab22f95f7f3482389cece07eec3338a2f4f4865139836349c75dfabc0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e3e8a8ab22f95f7f3482389cece07eec3338a2f4f4865139836349c75dfabc0d","first_computed_at":"2026-07-05T11:15:02.114882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:02.114882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Btm6Uok80Aqh/kuulStld/pIVYQB+Cl7O3h5kqm4cnlOkPMABA0zEBnsFj5g2eMROg0TDsj6Pfr6RVaulHfNAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:02.115301Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.02447","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba845675295a66c5085e67709060363fa3225f2069afd0d907858c5a9041bd4b","sha256:2a124d407c9194c790c9d14e1cddf1eab6e32e80d034f3020e978a1b98556360"],"state_sha256":"f9c62c2ce018626165493611ea70547f2a031ec7654d18f34d63be69c8c20087"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4va1Ir2eb6yJlSs4SguTQ7RUn0aCe+ZmPUfPFuSDjLlbbFIW6tj/vFAu0enU473sbRPtO3TbB4yt7lQc/9tUDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:36:03.113775Z","bundle_sha256":"b0a49ea9e0ddccdb9a12004ccb73d6cc7e37054b1ef6ae60ca9eceaebf501f7a"}}