{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BBMJKXES4NOWIVEMVHH3FNSLH2","short_pith_number":"pith:BBMJKXES","canonical_record":{"source":{"id":"2509.09735","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-10T14:25:09Z","cross_cats_sorted":[],"title_canon_sha256":"73b6f91fc4e4b746e9de6e410dd1c4957ca27285b1a60d0ef01c9406b16d6693","abstract_canon_sha256":"2d8ecd4d58db74c8dac69803213f58d27f3ddbf814f019cbdc4b40bdf5f8438d"},"schema_version":"1.0"},"canonical_sha256":"0858955c92e35d64548ca9cfb2b64b3e89f20201ba9249ca58ec59357e475886","source":{"kind":"arxiv","id":"2509.09735","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09735","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09735v1","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09735","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"BBMJKXES4NOW","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"pith_short_16","alias_value":"BBMJKXES4NOWIVEM","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"pith_short_8","alias_value":"BBMJKXES","created_at":"2026-07-05T12:09:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BBMJKXES4NOWIVEMVHH3FNSLH2","target":"record","payload":{"canonical_record":{"source":{"id":"2509.09735","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-10T14:25:09Z","cross_cats_sorted":[],"title_canon_sha256":"73b6f91fc4e4b746e9de6e410dd1c4957ca27285b1a60d0ef01c9406b16d6693","abstract_canon_sha256":"2d8ecd4d58db74c8dac69803213f58d27f3ddbf814f019cbdc4b40bdf5f8438d"},"schema_version":"1.0"},"canonical_sha256":"0858955c92e35d64548ca9cfb2b64b3e89f20201ba9249ca58ec59357e475886","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:48.323764Z","signature_b64":"6Tgl+AD4EQoH1Y116tL7x8VkVgZ/aZyvZ26bqDqco9/Tl0v39cq+qwYV03MZgUgms6wT95YrN3UwH5vdD1Q/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0858955c92e35d64548ca9cfb2b64b3e89f20201ba9249ca58ec59357e475886","last_reissued_at":"2026-07-05T12:09:48.323064Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:48.323064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.09735","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":"2026-07-05T12:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HMqj4ohSFS+/QDNNt3PpzQT/EoJgiubTxMuioNy8kbLH9U8QdHNGxsonIUDxm0gBZHOsV7S/lDXkUFmQdmthAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:35:07.230921Z"},"content_sha256":"1505d56aeb940aa0e493059fd86a1b49a8f52a7863ff650ea149f833a84ac5f5","schema_version":"1.0","event_id":"sha256:1505d56aeb940aa0e493059fd86a1b49a8f52a7863ff650ea149f833a84ac5f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BBMJKXES4NOWIVEMVHH3FNSLH2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Discrimination by LLMs: Cross-lingual Bias Assessment and Mitigation in Decision-Making and Summarisation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jieying Chen, Willem Huijzer","submitted_at":"2025-09-10T14:25:09Z","abstract_excerpt":"The rapid integration of Large Language Models (LLMs) into various domains raises concerns about societal inequalities and information bias. This study examines biases in LLMs related to background, gender, and age, with a focus on their impact on decision-making and summarization tasks. Additionally, the research examines the cross-lingual propagation of these biases and evaluates the effectiveness of prompt-instructed mitigation strategies. Using an adapted version of the dataset by Tamkin et al. (2023) translated into Dutch, we created 151,200 unique prompts for the decision task and 176,40"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09735","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/2509.09735/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":"2026-07-05T12:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qSztMUsucbyRtceDABnuy9PoTC9qu6d84Jejly8W48c5c6JeNPqziC8wWZNrIK62CypBuG5WfRU9D2iMU8oiDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:35:07.231831Z"},"content_sha256":"f6d1d294ae66237448061a0fd9bdc7ef02b8365edc683388ecb34ee5cf2eb7a8","schema_version":"1.0","event_id":"sha256:f6d1d294ae66237448061a0fd9bdc7ef02b8365edc683388ecb34ee5cf2eb7a8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BBMJKXES4NOWIVEMVHH3FNSLH2/bundle.json","state_url":"https://pith.science/pith/BBMJKXES4NOWIVEMVHH3FNSLH2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BBMJKXES4NOWIVEMVHH3FNSLH2/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":"2026-08-05T08:35:07Z","links":{"resolver":"https://pith.science/pith/BBMJKXES4NOWIVEMVHH3FNSLH2","bundle":"https://pith.science/pith/BBMJKXES4NOWIVEMVHH3FNSLH2/bundle.json","state":"https://pith.science/pith/BBMJKXES4NOWIVEMVHH3FNSLH2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BBMJKXES4NOWIVEMVHH3FNSLH2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BBMJKXES4NOWIVEMVHH3FNSLH2","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":"2d8ecd4d58db74c8dac69803213f58d27f3ddbf814f019cbdc4b40bdf5f8438d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-10T14:25:09Z","title_canon_sha256":"73b6f91fc4e4b746e9de6e410dd1c4957ca27285b1a60d0ef01c9406b16d6693"},"schema_version":"1.0","source":{"id":"2509.09735","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09735","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09735v1","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09735","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"BBMJKXES4NOW","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"pith_short_16","alias_value":"BBMJKXES4NOWIVEM","created_at":"2026-07-05T12:09:48Z"},{"alias_kind":"pith_short_8","alias_value":"BBMJKXES","created_at":"2026-07-05T12:09:48Z"}],"graph_snapshots":[{"event_id":"sha256:f6d1d294ae66237448061a0fd9bdc7ef02b8365edc683388ecb34ee5cf2eb7a8","target":"graph","created_at":"2026-07-05T12:09:48Z","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/2509.09735/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid integration of Large Language Models (LLMs) into various domains raises concerns about societal inequalities and information bias. This study examines biases in LLMs related to background, gender, and age, with a focus on their impact on decision-making and summarization tasks. Additionally, the research examines the cross-lingual propagation of these biases and evaluates the effectiveness of prompt-instructed mitigation strategies. Using an adapted version of the dataset by Tamkin et al. (2023) translated into Dutch, we created 151,200 unique prompts for the decision task and 176,40","authors_text":"Jieying Chen, Willem Huijzer","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-10T14:25:09Z","title":"Discrimination by LLMs: Cross-lingual Bias Assessment and Mitigation in Decision-Making and Summarisation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09735","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":"sha256:1505d56aeb940aa0e493059fd86a1b49a8f52a7863ff650ea149f833a84ac5f5","target":"record","created_at":"2026-07-05T12:09:48Z","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":"2d8ecd4d58db74c8dac69803213f58d27f3ddbf814f019cbdc4b40bdf5f8438d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-10T14:25:09Z","title_canon_sha256":"73b6f91fc4e4b746e9de6e410dd1c4957ca27285b1a60d0ef01c9406b16d6693"},"schema_version":"1.0","source":{"id":"2509.09735","kind":"arxiv","version":1}},"canonical_sha256":"0858955c92e35d64548ca9cfb2b64b3e89f20201ba9249ca58ec59357e475886","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0858955c92e35d64548ca9cfb2b64b3e89f20201ba9249ca58ec59357e475886","first_computed_at":"2026-07-05T12:09:48.323064Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:48.323064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6Tgl+AD4EQoH1Y116tL7x8VkVgZ/aZyvZ26bqDqco9/Tl0v39cq+qwYV03MZgUgms6wT95YrN3UwH5vdD1Q/CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:48.323764Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09735","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1505d56aeb940aa0e493059fd86a1b49a8f52a7863ff650ea149f833a84ac5f5","sha256:f6d1d294ae66237448061a0fd9bdc7ef02b8365edc683388ecb34ee5cf2eb7a8"],"state_sha256":"38247c4c8b17ffae2129d3a58d2e11206aac9f54a80d1c86767009e911c01382"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lp4Bzi1GuLdyeQmyWeImqCZxgIZBSiauQBzocQ92IK7ChZ/WFIKRUnOKbR8dDpxR0ocaoWtXpKiJnwd2IMVLDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T08:35:07.237360Z","bundle_sha256":"29b0518cd72aee8914019fd7ae016dabe27e3e03be79e20e44e527cf8fec6f57"}}