{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ILASQTHSEOJWMLXMCJ3E6J5QU2","short_pith_number":"pith:ILASQTHS","schema_version":"1.0","canonical_sha256":"42c1284cf22393662eec12764f27b0a6b6cd94c9009c538b0a9a8518ab9e4e6c","source":{"kind":"arxiv","id":"2312.07252","version":3},"attestation_state":"computed","paper":{"title":"Identifying Drivers of Predictive Aleatoric Uncertainty","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bernhard Y. Renard, Katharina Baum, Pascal Iversen, Simon Witzke","submitted_at":"2023-12-12T13:28:53Z","abstract_excerpt":"Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhancing transparent decision-making. So far, explanations of uncertainties have been rarely studied. The few exceptions rely on Bayesian neural networks or technically intricate approaches, such as auxiliary generative models, thereby hindering their broad adoption. We propose a straightforward appro"},"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":"2312.07252","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-12T13:28:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"86f4d95c16365aa35b665ea9d15416b3193e9338912fa2b317f9b6b255be57b8","abstract_canon_sha256":"1420012f0d040443a68d75f9e7af968340f14517d8ffc6bbff875303a4ee4073"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:22.681247Z","signature_b64":"m+WMyuKk7SGnCXC8llzdHz43ouwxVFufpDlOIczZ4P+/DFN8lpYscGyeyrFt2K86ou5t1sM2BC2AiLk3G82wCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42c1284cf22393662eec12764f27b0a6b6cd94c9009c538b0a9a8518ab9e4e6c","last_reissued_at":"2026-07-05T11:01:22.680806Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:22.680806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Drivers of Predictive Aleatoric Uncertainty","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bernhard Y. Renard, Katharina Baum, Pascal Iversen, Simon Witzke","submitted_at":"2023-12-12T13:28:53Z","abstract_excerpt":"Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhancing transparent decision-making. So far, explanations of uncertainties have been rarely studied. The few exceptions rely on Bayesian neural networks or technically intricate approaches, such as auxiliary generative models, thereby hindering their broad adoption. We propose a straightforward appro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07252","kind":"arxiv","version":3},"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/2312.07252/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":"2312.07252","created_at":"2026-07-05T11:01:22.680862+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.07252v3","created_at":"2026-07-05T11:01:22.680862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07252","created_at":"2026-07-05T11:01:22.680862+00:00"},{"alias_kind":"pith_short_12","alias_value":"ILASQTHSEOJW","created_at":"2026-07-05T11:01:22.680862+00:00"},{"alias_kind":"pith_short_16","alias_value":"ILASQTHSEOJWMLXM","created_at":"2026-07-05T11:01:22.680862+00:00"},{"alias_kind":"pith_short_8","alias_value":"ILASQTHS","created_at":"2026-07-05T11:01:22.680862+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.13118","citing_title":"Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2","json":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2.json","graph_json":"https://pith.science/api/pith-number/ILASQTHSEOJWMLXMCJ3E6J5QU2/graph.json","events_json":"https://pith.science/api/pith-number/ILASQTHSEOJWMLXMCJ3E6J5QU2/events.json","paper":"https://pith.science/paper/ILASQTHS"},"agent_actions":{"view_html":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2","download_json":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2.json","view_paper":"https://pith.science/paper/ILASQTHS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.07252&json=true","fetch_graph":"https://pith.science/api/pith-number/ILASQTHSEOJWMLXMCJ3E6J5QU2/graph.json","fetch_events":"https://pith.science/api/pith-number/ILASQTHSEOJWMLXMCJ3E6J5QU2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2/action/storage_attestation","attest_author":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2/action/author_attestation","sign_citation":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2/action/citation_signature","submit_replication":"https://pith.science/pith/ILASQTHSEOJWMLXMCJ3E6J5QU2/action/replication_record"}},"created_at":"2026-07-05T11:01:22.680862+00:00","updated_at":"2026-07-05T11:01:22.680862+00:00"}