{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:JNWG5JP6S5LSBMGDPON6ANUUTD","short_pith_number":"pith:JNWG5JP6","canonical_record":{"source":{"id":"2606.00019","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-14T00:10:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"22dad9da6c4e4cf2fa1fa2fa62ee91fc598290e549d697db7015d966293e46af","abstract_canon_sha256":"bb47a40c42e2927825ce2767bcc924285a71fd5fc51fe1171d82ed75e0b7dd1f"},"schema_version":"1.0"},"canonical_sha256":"4b6c6ea5fe975720b0c37b9be0369498c0124d9147b136366e0262ec5eb633a4","source":{"kind":"arxiv","id":"2606.00019","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00019","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00019v1","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00019","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"pith_short_12","alias_value":"JNWG5JP6S5LS","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"pith_short_16","alias_value":"JNWG5JP6S5LSBMGD","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"pith_short_8","alias_value":"JNWG5JP6","created_at":"2026-06-02T00:03:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:JNWG5JP6S5LSBMGDPON6ANUUTD","target":"record","payload":{"canonical_record":{"source":{"id":"2606.00019","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-14T00:10:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"22dad9da6c4e4cf2fa1fa2fa62ee91fc598290e549d697db7015d966293e46af","abstract_canon_sha256":"bb47a40c42e2927825ce2767bcc924285a71fd5fc51fe1171d82ed75e0b7dd1f"},"schema_version":"1.0"},"canonical_sha256":"4b6c6ea5fe975720b0c37b9be0369498c0124d9147b136366e0262ec5eb633a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T00:03:12.520012Z","signature_b64":"cYmD3d2wPPPuU6i2vrY9N3xckl+JIbjeYdnjXQN5O7ibLd/pYMm9dpC+kSR8koJH5/xzKs4yAE/6B8o+00QAAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b6c6ea5fe975720b0c37b9be0369498c0124d9147b136366e0262ec5eb633a4","last_reissued_at":"2026-06-02T00:03:12.519521Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T00:03:12.519521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.00019","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":"2026-06-02T00:03:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oThhKToeF0RnyIp832xiO87L6ZvDVTZL29oAaDnaKHuWeURIWQHuZEcZTCxt0IXChKpreLNr9dHVii4czD5/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T06:49:09.400290Z"},"content_sha256":"2e8171512fcb7731856c2c87a70afe6f3add6f283e285fa48564050d332c0465","schema_version":"1.0","event_id":"sha256:2e8171512fcb7731856c2c87a70afe6f3add6f283e285fa48564050d332c0465"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:JNWG5JP6S5LSBMGDPON6ANUUTD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Alexandra L. Beck, Archana J. McEligot, Danielle Perret, Deepti Pandita, Emilie Chow, Gelareh Sadigh, Jasmine Dhillon, Kai Zheng, Sairam Sutari, Steven Tam, Yawen Guo, Yiliang Zhou","submitted_at":"2026-04-14T00:10:39Z","abstract_excerpt":"Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear. We conducted a large-scale comparison analysis of AI drafts and corresponding clinician finalized notes to quantify stigmatizing language changes pre- and post-editing. Using a lexicon-based natural language processing (NLP) pipeline, we measured (1) the prevalence of stigmatizing language in AI drafts, (2) the prevalence and term composition in final notes, and (3) the frequency of removal or i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00019","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/2606.00019/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":"2026-06-02T00:03:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"haCXqjLIEMtOvvLze01Wv5YDk85tLiH0wFdPv7kwEVtDFlUZYhCrXNobH8d+SwV/+AHGfMJnrHX2PY38nflUAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T06:49:09.400698Z"},"content_sha256":"67a3bf6f2ff3952d7bf6ce94233fe5e9c4c6a581c7e93ac2d4ef308b5261299f","schema_version":"1.0","event_id":"sha256:67a3bf6f2ff3952d7bf6ce94233fe5e9c4c6a581c7e93ac2d4ef308b5261299f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JNWG5JP6S5LSBMGDPON6ANUUTD/bundle.json","state_url":"https://pith.science/pith/JNWG5JP6S5LSBMGDPON6ANUUTD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JNWG5JP6S5LSBMGDPON6ANUUTD/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":"2026-06-23T06:49:09Z","links":{"resolver":"https://pith.science/pith/JNWG5JP6S5LSBMGDPON6ANUUTD","bundle":"https://pith.science/pith/JNWG5JP6S5LSBMGDPON6ANUUTD/bundle.json","state":"https://pith.science/pith/JNWG5JP6S5LSBMGDPON6ANUUTD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JNWG5JP6S5LSBMGDPON6ANUUTD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JNWG5JP6S5LSBMGDPON6ANUUTD","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":"bb47a40c42e2927825ce2767bcc924285a71fd5fc51fe1171d82ed75e0b7dd1f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-14T00:10:39Z","title_canon_sha256":"22dad9da6c4e4cf2fa1fa2fa62ee91fc598290e549d697db7015d966293e46af"},"schema_version":"1.0","source":{"id":"2606.00019","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00019","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00019v1","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00019","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"pith_short_12","alias_value":"JNWG5JP6S5LS","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"pith_short_16","alias_value":"JNWG5JP6S5LSBMGD","created_at":"2026-06-02T00:03:12Z"},{"alias_kind":"pith_short_8","alias_value":"JNWG5JP6","created_at":"2026-06-02T00:03:12Z"}],"graph_snapshots":[{"event_id":"sha256:67a3bf6f2ff3952d7bf6ce94233fe5e9c4c6a581c7e93ac2d4ef308b5261299f","target":"graph","created_at":"2026-06-02T00:03:12Z","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/2606.00019/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear. We conducted a large-scale comparison analysis of AI drafts and corresponding clinician finalized notes to quantify stigmatizing language changes pre- and post-editing. Using a lexicon-based natural language processing (NLP) pipeline, we measured (1) the prevalence of stigmatizing language in AI drafts, (2) the prevalence and term composition in final notes, and (3) the frequency of removal or i","authors_text":"Alexandra L. Beck, Archana J. McEligot, Danielle Perret, Deepti Pandita, Emilie Chow, Gelareh Sadigh, Jasmine Dhillon, Kai Zheng, Sairam Sutari, Steven Tam, Yawen Guo, Yiliang Zhou","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-14T00:10:39Z","title":"Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00019","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":"sha256:2e8171512fcb7731856c2c87a70afe6f3add6f283e285fa48564050d332c0465","target":"record","created_at":"2026-06-02T00:03:12Z","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":"bb47a40c42e2927825ce2767bcc924285a71fd5fc51fe1171d82ed75e0b7dd1f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-14T00:10:39Z","title_canon_sha256":"22dad9da6c4e4cf2fa1fa2fa62ee91fc598290e549d697db7015d966293e46af"},"schema_version":"1.0","source":{"id":"2606.00019","kind":"arxiv","version":1}},"canonical_sha256":"4b6c6ea5fe975720b0c37b9be0369498c0124d9147b136366e0262ec5eb633a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b6c6ea5fe975720b0c37b9be0369498c0124d9147b136366e0262ec5eb633a4","first_computed_at":"2026-06-02T00:03:12.519521Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T00:03:12.519521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cYmD3d2wPPPuU6i2vrY9N3xckl+JIbjeYdnjXQN5O7ibLd/pYMm9dpC+kSR8koJH5/xzKs4yAE/6B8o+00QAAA==","signature_status":"signed_v1","signed_at":"2026-06-02T00:03:12.520012Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.00019","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e8171512fcb7731856c2c87a70afe6f3add6f283e285fa48564050d332c0465","sha256:67a3bf6f2ff3952d7bf6ce94233fe5e9c4c6a581c7e93ac2d4ef308b5261299f"],"state_sha256":"d1942b942f9e793c5d0b360cef17e4debf1a68ea7c2e583e3608e0cbeefb9fab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hIE3NsrM5pdELaGaJyHbzVQZZYjQSVfpCMu9B3r9JMNK2DU1RHmK7peY3c1UR4AD+0K2rhgUPjw/LHo/Hl9pBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T06:49:09.402734Z","bundle_sha256":"62d0e10f8efe7b1c3b028b06cca5888328e33a50e28d474e8b3405fb939750eb"}}