{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:R5T6XBRMXNQTUK2LAFCHMGYA7K","short_pith_number":"pith:R5T6XBRM","canonical_record":{"source":{"id":"2506.11361","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-12T23:33:42Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"c1576d0a75877d64e69105acfcadbac2db03b16aab41b021cd64bc6ae471b7a4","abstract_canon_sha256":"0b63717f1e136889c10f7715695ada8c66e179b6717fbf0fdd6e2a39cec869e8"},"schema_version":"1.0"},"canonical_sha256":"8f67eb862cbb613a2b4b0144761b00fa868e5399619dd808781edf485e0a429a","source":{"kind":"arxiv","id":"2506.11361","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11361","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11361v1","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11361","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_12","alias_value":"R5T6XBRMXNQT","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_16","alias_value":"R5T6XBRMXNQTUK2L","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_8","alias_value":"R5T6XBRM","created_at":"2026-07-05T11:21:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:R5T6XBRMXNQTUK2LAFCHMGYA7K","target":"record","payload":{"canonical_record":{"source":{"id":"2506.11361","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-12T23:33:42Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"c1576d0a75877d64e69105acfcadbac2db03b16aab41b021cd64bc6ae471b7a4","abstract_canon_sha256":"0b63717f1e136889c10f7715695ada8c66e179b6717fbf0fdd6e2a39cec869e8"},"schema_version":"1.0"},"canonical_sha256":"8f67eb862cbb613a2b4b0144761b00fa868e5399619dd808781edf485e0a429a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:02.986398Z","signature_b64":"oxEgtzjyn1CUUzgrCjUM1jDWnLUFqhMgDyFReuO1UesvK93M92xPnXa698T4M44eMQpO23+z3i3WDZk/vvjtAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f67eb862cbb613a2b4b0144761b00fa868e5399619dd808781edf485e0a429a","last_reissued_at":"2026-07-05T11:21:02.986005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:02.986005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.11361","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":"2026-07-05T11:21:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CGm7PrZ4Y/0/RMkx5DVDemnrGx8PUqjZTUgBVkFBZ480DwzE03CVDzus1WXV9WMjWYLIeYSxXfP5p8xINWZUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:12:32.177052Z"},"content_sha256":"879e9e0029507896dd2adf3260b18b81f0732a89df75987a32647e478187a866","schema_version":"1.0","event_id":"sha256:879e9e0029507896dd2adf3260b18b81f0732a89df75987a32647e478187a866"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:R5T6XBRMXNQTUK2LAFCHMGYA7K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Biased Samaritan: LLM biases in Perceived Kindness","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.CL","authors_text":"Amy Yue-Ming Yu, Jack H Fagan, Ruhaan Juyaal, Siya Pun","submitted_at":"2025-06-12T23:33:42Z","abstract_excerpt":"While Large Language Models (LLMs) have become ubiquitous in many fields, understanding and mitigating LLM biases is an ongoing issue. This paper provides a novel method for evaluating the demographic biases of various generative AI models. By prompting models to assess a moral patient's willingness to intervene constructively, we aim to quantitatively evaluate different LLMs' biases towards various genders, races, and ages. Our work differs from existing work by aiming to determine the baseline demographic identities for various commercial models and the relationship between the baseline and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11361","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/2506.11361/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":"2026-07-05T11:21:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ikqn97bVEMRRmVHgU7cB0cyzKFZcoLMwsvZlz+qEdKhDoVPUhR5K36+07kuvR6IhjBZj4HOBnH3yr8mme9uuDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:12:32.177556Z"},"content_sha256":"d0ce8d1a76ae6530441758ee06af5d9e50937369ecca9fa5bc36e79205e41828","schema_version":"1.0","event_id":"sha256:d0ce8d1a76ae6530441758ee06af5d9e50937369ecca9fa5bc36e79205e41828"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K/bundle.json","state_url":"https://pith.science/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K/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":"2026-08-08T20:12:32Z","links":{"resolver":"https://pith.science/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K","bundle":"https://pith.science/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K/bundle.json","state":"https://pith.science/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R5T6XBRMXNQTUK2LAFCHMGYA7K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:R5T6XBRMXNQTUK2LAFCHMGYA7K","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":"0b63717f1e136889c10f7715695ada8c66e179b6717fbf0fdd6e2a39cec869e8","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-12T23:33:42Z","title_canon_sha256":"c1576d0a75877d64e69105acfcadbac2db03b16aab41b021cd64bc6ae471b7a4"},"schema_version":"1.0","source":{"id":"2506.11361","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11361","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11361v1","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11361","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_12","alias_value":"R5T6XBRMXNQT","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_16","alias_value":"R5T6XBRMXNQTUK2L","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_8","alias_value":"R5T6XBRM","created_at":"2026-07-05T11:21:02Z"}],"graph_snapshots":[{"event_id":"sha256:d0ce8d1a76ae6530441758ee06af5d9e50937369ecca9fa5bc36e79205e41828","target":"graph","created_at":"2026-07-05T11:21:02Z","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/2506.11361/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Large Language Models (LLMs) have become ubiquitous in many fields, understanding and mitigating LLM biases is an ongoing issue. This paper provides a novel method for evaluating the demographic biases of various generative AI models. By prompting models to assess a moral patient's willingness to intervene constructively, we aim to quantitatively evaluate different LLMs' biases towards various genders, races, and ages. Our work differs from existing work by aiming to determine the baseline demographic identities for various commercial models and the relationship between the baseline and ","authors_text":"Amy Yue-Ming Yu, Jack H Fagan, Ruhaan Juyaal, Siya Pun","cross_cats":["cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-12T23:33:42Z","title":"The Biased Samaritan: LLM biases in Perceived Kindness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11361","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":"sha256:879e9e0029507896dd2adf3260b18b81f0732a89df75987a32647e478187a866","target":"record","created_at":"2026-07-05T11:21:02Z","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":"0b63717f1e136889c10f7715695ada8c66e179b6717fbf0fdd6e2a39cec869e8","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-12T23:33:42Z","title_canon_sha256":"c1576d0a75877d64e69105acfcadbac2db03b16aab41b021cd64bc6ae471b7a4"},"schema_version":"1.0","source":{"id":"2506.11361","kind":"arxiv","version":1}},"canonical_sha256":"8f67eb862cbb613a2b4b0144761b00fa868e5399619dd808781edf485e0a429a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f67eb862cbb613a2b4b0144761b00fa868e5399619dd808781edf485e0a429a","first_computed_at":"2026-07-05T11:21:02.986005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:02.986005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oxEgtzjyn1CUUzgrCjUM1jDWnLUFqhMgDyFReuO1UesvK93M92xPnXa698T4M44eMQpO23+z3i3WDZk/vvjtAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:02.986398Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.11361","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:879e9e0029507896dd2adf3260b18b81f0732a89df75987a32647e478187a866","sha256:d0ce8d1a76ae6530441758ee06af5d9e50937369ecca9fa5bc36e79205e41828"],"state_sha256":"23bd9c6717352abe067ab627094675537701770913356c985244c069f1e98797"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SKhSuiiXvcF1NqlvP4KC0pJ1oSwaaJnHUUc+TejqBVXx/0g/9hwBqgcXeU63h22nCh611ogJdlEyUvAuQcsrDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:12:32.182278Z","bundle_sha256":"906c3d16cf6fd2526c6b8d4c3a72b751841c267f483cf60c97a08cdcfb98a5a7"}}