{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4363PYX6SXVJIF2OTBEMUKUISL","short_pith_number":"pith:4363PYX6","schema_version":"1.0","canonical_sha256":"e6fdb7e2fe95ea94174e9848ca2a8892c4149c9873acda5a597ef34519fd97ec","source":{"kind":"arxiv","id":"2403.00011","version":1},"attestation_state":"computed","paper":{"title":"Introducing User Feedback-based Counterfactual Explanations (UFCE)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Alessandro Bogliolo, Jose M. Alonso-Moral, Muhammad Suffian","submitted_at":"2024-02-26T20:09:44Z","abstract_excerpt":"Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimizing changes to turn over an undesired outcome, overlookin"},"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":"2403.00011","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T20:09:44Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"16026c9cbed5a62f3f7cf6a599a7a290b6b988233f1c04843c85cb1e479b5db5","abstract_canon_sha256":"3b29435f9c3047fca28c902b295b191647c52c38f66510f0a673faa37ff272ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:57.475698Z","signature_b64":"CRyMcBIlsJWIYg5ldEy3wMhb0n9WgNwrEuyPUpq9cOMWwui2Kgm5QKR4N84IdUo0bks5edwt402Ya49MF+LlDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6fdb7e2fe95ea94174e9848ca2a8892c4149c9873acda5a597ef34519fd97ec","last_reissued_at":"2026-07-05T07:50:57.475286Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:57.475286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Introducing User Feedback-based Counterfactual Explanations (UFCE)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Alessandro Bogliolo, Jose M. Alonso-Moral, Muhammad Suffian","submitted_at":"2024-02-26T20:09:44Z","abstract_excerpt":"Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimizing changes to turn over an undesired outcome, overlookin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.00011","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/2403.00011/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":"2403.00011","created_at":"2026-07-05T07:50:57.475354+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.00011v1","created_at":"2026-07-05T07:50:57.475354+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.00011","created_at":"2026-07-05T07:50:57.475354+00:00"},{"alias_kind":"pith_short_12","alias_value":"4363PYX6SXVJ","created_at":"2026-07-05T07:50:57.475354+00:00"},{"alias_kind":"pith_short_16","alias_value":"4363PYX6SXVJIF2O","created_at":"2026-07-05T07:50:57.475354+00:00"},{"alias_kind":"pith_short_8","alias_value":"4363PYX6","created_at":"2026-07-05T07:50:57.475354+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03042","citing_title":"Dynamic Long Short-Term Memory Based Memory Storage For Long Horizon LLM Interaction","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL","json":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL.json","graph_json":"https://pith.science/api/pith-number/4363PYX6SXVJIF2OTBEMUKUISL/graph.json","events_json":"https://pith.science/api/pith-number/4363PYX6SXVJIF2OTBEMUKUISL/events.json","paper":"https://pith.science/paper/4363PYX6"},"agent_actions":{"view_html":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL","download_json":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL.json","view_paper":"https://pith.science/paper/4363PYX6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.00011&json=true","fetch_graph":"https://pith.science/api/pith-number/4363PYX6SXVJIF2OTBEMUKUISL/graph.json","fetch_events":"https://pith.science/api/pith-number/4363PYX6SXVJIF2OTBEMUKUISL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL/action/storage_attestation","attest_author":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL/action/author_attestation","sign_citation":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL/action/citation_signature","submit_replication":"https://pith.science/pith/4363PYX6SXVJIF2OTBEMUKUISL/action/replication_record"}},"created_at":"2026-07-05T07:50:57.475354+00:00","updated_at":"2026-07-05T07:50:57.475354+00:00"}