{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MD2JG3VM33JYJ3FE4HJISNSU33","short_pith_number":"pith:MD2JG3VM","schema_version":"1.0","canonical_sha256":"60f4936eacded384eca4e1d2893654def56bdbc4290ffdfa9941cd6610d57dba","source":{"kind":"arxiv","id":"2303.17249","version":4},"attestation_state":"computed","paper":{"title":"Model-agnostic explainable artificial intelligence for object detection in image data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"David Colwell, Ke Yan, Matthias Samwald, Milad Moradi, Rhona Asgari","submitted_at":"2023-03-30T09:29:03Z","abstract_excerpt":"In recent years, deep neural networks have been widely used for building high-performance Artificial Intelligence (AI) systems for computer vision applications. Object detection is a fundamental task in computer vision, which has been greatly progressed through developing large and intricate AI models. However, the lack of transparency is a big challenge that may not allow the widespread adoption of these models. Explainable artificial intelligence is a field of research where methods are developed to help users understand the behavior, decision logics, and vulnerabilities of AI systems. Previ"},"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":"2303.17249","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-30T09:29:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ce3bc73b777cbbf98fe37ed95db6ad3bcc8ed5a489be99ac84822d38c9e761b9","abstract_canon_sha256":"75eba633001754290efa3c9f6d583d22a74f0cd8dcb890fe6e0959f4d46d9c01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:02:53.519415Z","signature_b64":"R+vqGoECKXeyKJBSbEA248mOeXdrNtG/ka2wgmxL/R4X0z7sFOJf2OPAnSRB0p4qlGa70b8f89goxqyStFJbDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60f4936eacded384eca4e1d2893654def56bdbc4290ffdfa9941cd6610d57dba","last_reissued_at":"2026-07-05T09:02:53.518614Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:02:53.518614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model-agnostic explainable artificial intelligence for object detection in image data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"David Colwell, Ke Yan, Matthias Samwald, Milad Moradi, Rhona Asgari","submitted_at":"2023-03-30T09:29:03Z","abstract_excerpt":"In recent years, deep neural networks have been widely used for building high-performance Artificial Intelligence (AI) systems for computer vision applications. Object detection is a fundamental task in computer vision, which has been greatly progressed through developing large and intricate AI models. However, the lack of transparency is a big challenge that may not allow the widespread adoption of these models. Explainable artificial intelligence is a field of research where methods are developed to help users understand the behavior, decision logics, and vulnerabilities of AI systems. Previ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.17249","kind":"arxiv","version":4},"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/2303.17249/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":"2303.17249","created_at":"2026-07-05T09:02:53.518681+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.17249v4","created_at":"2026-07-05T09:02:53.518681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.17249","created_at":"2026-07-05T09:02:53.518681+00:00"},{"alias_kind":"pith_short_12","alias_value":"MD2JG3VM33JY","created_at":"2026-07-05T09:02:53.518681+00:00"},{"alias_kind":"pith_short_16","alias_value":"MD2JG3VM33JYJ3FE","created_at":"2026-07-05T09:02:53.518681+00:00"},{"alias_kind":"pith_short_8","alias_value":"MD2JG3VM","created_at":"2026-07-05T09:02:53.518681+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.06253","citing_title":"The State of Post-Hoc Local XAI Techniques for Image Processing: Challenges and Motivations","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33","json":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33.json","graph_json":"https://pith.science/api/pith-number/MD2JG3VM33JYJ3FE4HJISNSU33/graph.json","events_json":"https://pith.science/api/pith-number/MD2JG3VM33JYJ3FE4HJISNSU33/events.json","paper":"https://pith.science/paper/MD2JG3VM"},"agent_actions":{"view_html":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33","download_json":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33.json","view_paper":"https://pith.science/paper/MD2JG3VM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.17249&json=true","fetch_graph":"https://pith.science/api/pith-number/MD2JG3VM33JYJ3FE4HJISNSU33/graph.json","fetch_events":"https://pith.science/api/pith-number/MD2JG3VM33JYJ3FE4HJISNSU33/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33/action/storage_attestation","attest_author":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33/action/author_attestation","sign_citation":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33/action/citation_signature","submit_replication":"https://pith.science/pith/MD2JG3VM33JYJ3FE4HJISNSU33/action/replication_record"}},"created_at":"2026-07-05T09:02:53.518681+00:00","updated_at":"2026-07-05T09:02:53.518681+00:00"}