{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:A4ERYAXIUONI4NOWRN5TBLEUP4","short_pith_number":"pith:A4ERYAXI","schema_version":"1.0","canonical_sha256":"07091c02e8a39a8e35d68b7b30ac947f2ad63f6623db008ae7773e2524798d06","source":{"kind":"arxiv","id":"2306.01941","version":2},"attestation_state":"computed","paper":{"title":"AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.HC","authors_text":"Jennifer Wortman Vaughan, Q. Vera Liao","submitted_at":"2023-06-02T22:51:26Z","abstract_excerpt":"The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI -- transparency -- is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.01941","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-06-02T22:51:26Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"ce2bcb309e07f6708e024bdf5c3e9034b1fc3d16739e76554284675541b0f6d0","abstract_canon_sha256":"0699a2545b58a8fded32b375c4f4fe4b926d21a12a35804895f9c5eed46e68df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:59.086233Z","signature_b64":"sIZAE0CjmY5MDKmJrxuqkztP0C8p02Ng7lnTBhYAbiIL7nx4Ze5dvg3ATIwE3SKQ4sPzxX3RD00uoJ9Li9xyAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07091c02e8a39a8e35d68b7b30ac947f2ad63f6623db008ae7773e2524798d06","last_reissued_at":"2026-07-05T06:38:59.085724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:59.085724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.HC","authors_text":"Jennifer Wortman Vaughan, Q. Vera Liao","submitted_at":"2023-06-02T22:51:26Z","abstract_excerpt":"The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI -- transparency -- is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.01941","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/2306.01941/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.01941","created_at":"2026-07-05T06:38:59.085791+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.01941v2","created_at":"2026-07-05T06:38:59.085791+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.01941","created_at":"2026-07-05T06:38:59.085791+00:00"},{"alias_kind":"pith_short_12","alias_value":"A4ERYAXIUONI","created_at":"2026-07-05T06:38:59.085791+00:00"},{"alias_kind":"pith_short_16","alias_value":"A4ERYAXIUONI4NOW","created_at":"2026-07-05T06:38:59.085791+00:00"},{"alias_kind":"pith_short_8","alias_value":"A4ERYAXI","created_at":"2026-07-05T06:38:59.085791+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":13,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.22177","citing_title":"Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15455","citing_title":"Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15326","citing_title":"Analyzing the Presentation, Content, and Utilization of References in LLM-powered Conversational AI Systems","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12809","citing_title":"Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces","ref_index":189,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05682","citing_title":"PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05682","citing_title":"PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19971","citing_title":"Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10473","citing_title":"AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07121","citing_title":"Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06385","citing_title":"Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17843","citing_title":"Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02765","citing_title":"U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06640","citing_title":"Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4","json":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4.json","graph_json":"https://pith.science/api/pith-number/A4ERYAXIUONI4NOWRN5TBLEUP4/graph.json","events_json":"https://pith.science/api/pith-number/A4ERYAXIUONI4NOWRN5TBLEUP4/events.json","paper":"https://pith.science/paper/A4ERYAXI"},"agent_actions":{"view_html":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4","download_json":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4.json","view_paper":"https://pith.science/paper/A4ERYAXI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.01941&json=true","fetch_graph":"https://pith.science/api/pith-number/A4ERYAXIUONI4NOWRN5TBLEUP4/graph.json","fetch_events":"https://pith.science/api/pith-number/A4ERYAXIUONI4NOWRN5TBLEUP4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4/action/storage_attestation","attest_author":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4/action/author_attestation","sign_citation":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4/action/citation_signature","submit_replication":"https://pith.science/pith/A4ERYAXIUONI4NOWRN5TBLEUP4/action/replication_record"}},"created_at":"2026-07-05T06:38:59.085791+00:00","updated_at":"2026-07-05T06:38:59.085791+00:00"}