{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SGVD6ZDF6HXAN2OMEEFCD245V4","short_pith_number":"pith:SGVD6ZDF","canonical_record":{"source":{"id":"2406.12033","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T19:05:32Z","cross_cats_sorted":[],"title_canon_sha256":"87bb7c8136fc7bd44aee2e0fb12ee07a117dc3be78d211810cf1b8cfdd97bc8d","abstract_canon_sha256":"28777a6d760214312f673edf18ee47ff5a5cc4d1fc3fdb043c9088abff77f14f"},"schema_version":"1.0"},"canonical_sha256":"91aa3f6465f1ee06e9cc210a21eb9daf25a7bec0621a2cf17d53f4179adfffec","source":{"kind":"arxiv","id":"2406.12033","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.12033","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"arxiv_version","alias_value":"2406.12033v2","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12033","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"pith_short_12","alias_value":"SGVD6ZDF6HXA","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"pith_short_16","alias_value":"SGVD6ZDF6HXAN2OM","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"pith_short_8","alias_value":"SGVD6ZDF","created_at":"2026-07-05T08:34:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SGVD6ZDF6HXAN2OMEEFCD245V4","target":"record","payload":{"canonical_record":{"source":{"id":"2406.12033","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T19:05:32Z","cross_cats_sorted":[],"title_canon_sha256":"87bb7c8136fc7bd44aee2e0fb12ee07a117dc3be78d211810cf1b8cfdd97bc8d","abstract_canon_sha256":"28777a6d760214312f673edf18ee47ff5a5cc4d1fc3fdb043c9088abff77f14f"},"schema_version":"1.0"},"canonical_sha256":"91aa3f6465f1ee06e9cc210a21eb9daf25a7bec0621a2cf17d53f4179adfffec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:21.940074Z","signature_b64":"gCVGXLjnzCeLTq4/9i/cnAluere7DtXkCB4TFagIc5cZJtW6eYU+r4MbG+n1Oyoo+XqI20fkm9jlMbTPEfkaCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91aa3f6465f1ee06e9cc210a21eb9daf25a7bec0621a2cf17d53f4179adfffec","last_reissued_at":"2026-07-05T08:34:21.939599Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:21.939599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.12033","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:34:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hr0mO5SP3DD2WS1wTEpvzPlwTJP9/GKzb5S8ADK9MuA96MPr8a5XclLjQlvZ420LI4l9vlnksxhFGV9iBGNADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:42:01.871280Z"},"content_sha256":"0b94d7f295359c7bb075f969e57afabdb2ae68452a71862313651dbaf1b3edba","schema_version":"1.0","event_id":"sha256:0b94d7f295359c7bb075f969e57afabdb2ae68452a71862313651dbaf1b3edba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SGVD6ZDF6HXAN2OMEEFCD245V4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anne de Hond, Malvika Pillai, Marieke M. van Buchem, Sara Alessandra Keller, Tina Hernandez-Boussard, Yun Zhao, Yuqing Wang","submitted_at":"2024-06-17T19:05:32Z","abstract_excerpt":"The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have focused on predictive performance, leaving the critical issue of fairness underexplored, posing significant risks to vulnerable populations. Despite acknowledging potential biases, previous works have lacked thorough investigations into these biases and their impacts. To address this gap, we systematically evaluate biases across seven social factors (e.g., gender, age, religion) using ten LLMs with different prompting"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12033","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/2406.12033/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":"2026-07-05T08:34:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yt6/fJzS2A5CvWjx6i3eGHI6SguvvlP9dV+eHbzE1sJVhtjKB0sawoVA9Q/3j8aX7VUhL4NH3592PdhOBVMsCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:42:01.871814Z"},"content_sha256":"5358bc37170731704cfdd1b16ed43e4e1094519e71a0175c725da659bb1514b3","schema_version":"1.0","event_id":"sha256:5358bc37170731704cfdd1b16ed43e4e1094519e71a0175c725da659bb1514b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SGVD6ZDF6HXAN2OMEEFCD245V4/bundle.json","state_url":"https://pith.science/pith/SGVD6ZDF6HXAN2OMEEFCD245V4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SGVD6ZDF6HXAN2OMEEFCD245V4/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":"2026-08-08T20:42:01Z","links":{"resolver":"https://pith.science/pith/SGVD6ZDF6HXAN2OMEEFCD245V4","bundle":"https://pith.science/pith/SGVD6ZDF6HXAN2OMEEFCD245V4/bundle.json","state":"https://pith.science/pith/SGVD6ZDF6HXAN2OMEEFCD245V4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SGVD6ZDF6HXAN2OMEEFCD245V4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SGVD6ZDF6HXAN2OMEEFCD245V4","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":"28777a6d760214312f673edf18ee47ff5a5cc4d1fc3fdb043c9088abff77f14f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T19:05:32Z","title_canon_sha256":"87bb7c8136fc7bd44aee2e0fb12ee07a117dc3be78d211810cf1b8cfdd97bc8d"},"schema_version":"1.0","source":{"id":"2406.12033","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.12033","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"arxiv_version","alias_value":"2406.12033v2","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12033","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"pith_short_12","alias_value":"SGVD6ZDF6HXA","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"pith_short_16","alias_value":"SGVD6ZDF6HXAN2OM","created_at":"2026-07-05T08:34:21Z"},{"alias_kind":"pith_short_8","alias_value":"SGVD6ZDF","created_at":"2026-07-05T08:34:21Z"}],"graph_snapshots":[{"event_id":"sha256:5358bc37170731704cfdd1b16ed43e4e1094519e71a0175c725da659bb1514b3","target":"graph","created_at":"2026-07-05T08:34:21Z","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/2406.12033/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have focused on predictive performance, leaving the critical issue of fairness underexplored, posing significant risks to vulnerable populations. Despite acknowledging potential biases, previous works have lacked thorough investigations into these biases and their impacts. To address this gap, we systematically evaluate biases across seven social factors (e.g., gender, age, religion) using ten LLMs with different prompting","authors_text":"Anne de Hond, Malvika Pillai, Marieke M. van Buchem, Sara Alessandra Keller, Tina Hernandez-Boussard, Yun Zhao, Yuqing Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T19:05:32Z","title":"Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12033","kind":"arxiv","version":2},"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":"sha256:0b94d7f295359c7bb075f969e57afabdb2ae68452a71862313651dbaf1b3edba","target":"record","created_at":"2026-07-05T08:34:21Z","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":"28777a6d760214312f673edf18ee47ff5a5cc4d1fc3fdb043c9088abff77f14f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T19:05:32Z","title_canon_sha256":"87bb7c8136fc7bd44aee2e0fb12ee07a117dc3be78d211810cf1b8cfdd97bc8d"},"schema_version":"1.0","source":{"id":"2406.12033","kind":"arxiv","version":2}},"canonical_sha256":"91aa3f6465f1ee06e9cc210a21eb9daf25a7bec0621a2cf17d53f4179adfffec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91aa3f6465f1ee06e9cc210a21eb9daf25a7bec0621a2cf17d53f4179adfffec","first_computed_at":"2026-07-05T08:34:21.939599Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:34:21.939599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gCVGXLjnzCeLTq4/9i/cnAluere7DtXkCB4TFagIc5cZJtW6eYU+r4MbG+n1Oyoo+XqI20fkm9jlMbTPEfkaCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:34:21.940074Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.12033","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b94d7f295359c7bb075f969e57afabdb2ae68452a71862313651dbaf1b3edba","sha256:5358bc37170731704cfdd1b16ed43e4e1094519e71a0175c725da659bb1514b3"],"state_sha256":"441ef54be9969c273ef2854462cf46ce315a573cb49838bfb1ae59844fd33721"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LSBFbE0kZdHZ2yEixGBS+Y9dB6oPk5w7O4sQpHPEyX9TwRsAv428gWo6O7wc17TUaWL/541m7X9jgXJK02aDDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:42:01.877084Z","bundle_sha256":"06ad39542ccae4f0496c986af302943e7e0d339054d6401737fea1ebc8680e4f"}}