{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:E6GPHPTYVRKNRD4T66Q466LSL5","short_pith_number":"pith:E6GPHPTY","schema_version":"1.0","canonical_sha256":"278cf3be78ac54d88f93f7a1cf79725f4478b6cc632e1f63a299f0b548f5b3fd","source":{"kind":"arxiv","id":"2211.02405","version":1},"attestation_state":"computed","paper":{"title":"Explainable Information Retrieval: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Avishek Anand, Jonas Wallat, Lijun Lyu, Maximilian Idahl, Yumeng Wang, Zijian Zhang","submitted_at":"2022-11-04T12:26:25Z","abstract_excerpt":"Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying pers"},"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":"2211.02405","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-11-04T12:26:25Z","cross_cats_sorted":[],"title_canon_sha256":"60b296f2d6285a2a17020265065cdcfae69a45c1b64e2b00974a0e833bd0ca5c","abstract_canon_sha256":"845c6e0a6c982f272de5c77f17dc501df693c6882aaf3953bc12ef124a7341ec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:15.000961Z","signature_b64":"/bJ89TSyHmGvWlcUpbh3cMCyhD6Tue7N5rts9IjVn/2qaRIdyB01pYARTeTLbO9JkcWufiB6QElEU8qqAv9qCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"278cf3be78ac54d88f93f7a1cf79725f4478b6cc632e1f63a299f0b548f5b3fd","last_reissued_at":"2026-07-05T05:13:15.000491Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:15.000491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainable Information Retrieval: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Avishek Anand, Jonas Wallat, Lijun Lyu, Maximilian Idahl, Yumeng Wang, Zijian Zhang","submitted_at":"2022-11-04T12:26:25Z","abstract_excerpt":"Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying pers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02405","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/2211.02405/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":"2211.02405","created_at":"2026-07-05T05:13:15.000548+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.02405v1","created_at":"2026-07-05T05:13:15.000548+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02405","created_at":"2026-07-05T05:13:15.000548+00:00"},{"alias_kind":"pith_short_12","alias_value":"E6GPHPTYVRKN","created_at":"2026-07-05T05:13:15.000548+00:00"},{"alias_kind":"pith_short_16","alias_value":"E6GPHPTYVRKNRD4T","created_at":"2026-07-05T05:13:15.000548+00:00"},{"alias_kind":"pith_short_8","alias_value":"E6GPHPTY","created_at":"2026-07-05T05:13:15.000548+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30984","citing_title":"Towards Critical IR Theories and Practices","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2411.05572","citing_title":"Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19628","citing_title":"Understanding Wacky Weights: A Dissection of SPLADE's Learned Term Importance","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2601.08919","citing_title":"LLMs as Assessors: Right for the Right Reason?","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2601.17230","citing_title":"CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5","json":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5.json","graph_json":"https://pith.science/api/pith-number/E6GPHPTYVRKNRD4T66Q466LSL5/graph.json","events_json":"https://pith.science/api/pith-number/E6GPHPTYVRKNRD4T66Q466LSL5/events.json","paper":"https://pith.science/paper/E6GPHPTY"},"agent_actions":{"view_html":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5","download_json":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5.json","view_paper":"https://pith.science/paper/E6GPHPTY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.02405&json=true","fetch_graph":"https://pith.science/api/pith-number/E6GPHPTYVRKNRD4T66Q466LSL5/graph.json","fetch_events":"https://pith.science/api/pith-number/E6GPHPTYVRKNRD4T66Q466LSL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5/action/storage_attestation","attest_author":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5/action/author_attestation","sign_citation":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5/action/citation_signature","submit_replication":"https://pith.science/pith/E6GPHPTYVRKNRD4T66Q466LSL5/action/replication_record"}},"created_at":"2026-07-05T05:13:15.000548+00:00","updated_at":"2026-07-05T05:13:15.000548+00:00"}