{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GCS542CLOLQIZESZNYFKBJMO2N","short_pith_number":"pith:GCS542CL","schema_version":"1.0","canonical_sha256":"30a5de684b72e08c92596e0aa0a58ed35a3e72fc0f8efbcc46952e333d682ec4","source":{"kind":"arxiv","id":"2309.13965","version":2},"attestation_state":"computed","paper":{"title":"May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Boyang Li, Tong Zhang, X. Jessie Yang","submitted_at":"2023-09-25T09:00:38Z","abstract_excerpt":"Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. Participants are presented with static explanations, followed by a conversation with a human expert regarding the explanations. We"},"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":"2309.13965","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-09-25T09:00:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bb0002c09feb73da7550d8b5137c132c42b23f5d4229ae35fc6a5e45f7ec6895","abstract_canon_sha256":"712317e8c9e92289f063d279c83ae34d55f6f6edede35db0e20f4c72e76fba61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:03.922247Z","signature_b64":"e91M02NWMGps+GQURtDrvA2MLWxcCCxsfDb8QxbYUyHDJkdYvl3+/xdD0+z+H6kUCs/GTxNf/kbp/yWJGyqUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30a5de684b72e08c92596e0aa0a58ed35a3e72fc0f8efbcc46952e333d682ec4","last_reissued_at":"2026-07-05T08:54:03.921848Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:03.921848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Boyang Li, Tong Zhang, X. Jessie Yang","submitted_at":"2023-09-25T09:00:38Z","abstract_excerpt":"Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. Participants are presented with static explanations, followed by a conversation with a human expert regarding the explanations. We"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.13965","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/2309.13965/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":"2309.13965","created_at":"2026-07-05T08:54:03.921904+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.13965v2","created_at":"2026-07-05T08:54:03.921904+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.13965","created_at":"2026-07-05T08:54:03.921904+00:00"},{"alias_kind":"pith_short_12","alias_value":"GCS542CLOLQI","created_at":"2026-07-05T08:54:03.921904+00:00"},{"alias_kind":"pith_short_16","alias_value":"GCS542CLOLQIZESZ","created_at":"2026-07-05T08:54:03.921904+00:00"},{"alias_kind":"pith_short_8","alias_value":"GCS542CL","created_at":"2026-07-05T08:54:03.921904+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.20312","citing_title":"Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions","ref_index":76,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N","json":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N.json","graph_json":"https://pith.science/api/pith-number/GCS542CLOLQIZESZNYFKBJMO2N/graph.json","events_json":"https://pith.science/api/pith-number/GCS542CLOLQIZESZNYFKBJMO2N/events.json","paper":"https://pith.science/paper/GCS542CL"},"agent_actions":{"view_html":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N","download_json":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N.json","view_paper":"https://pith.science/paper/GCS542CL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.13965&json=true","fetch_graph":"https://pith.science/api/pith-number/GCS542CLOLQIZESZNYFKBJMO2N/graph.json","fetch_events":"https://pith.science/api/pith-number/GCS542CLOLQIZESZNYFKBJMO2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N/action/storage_attestation","attest_author":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N/action/author_attestation","sign_citation":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N/action/citation_signature","submit_replication":"https://pith.science/pith/GCS542CLOLQIZESZNYFKBJMO2N/action/replication_record"}},"created_at":"2026-07-05T08:54:03.921904+00:00","updated_at":"2026-07-05T08:54:03.921904+00:00"}