{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OHTER2RVBENW3PE6CZPIQHQTTE","short_pith_number":"pith:OHTER2RV","schema_version":"1.0","canonical_sha256":"71e648ea35091b6dbc9e165e881e1399053e06c5501e0428747d4d932332453d","source":{"kind":"arxiv","id":"2203.02399","version":4},"attestation_state":"computed","paper":{"title":"Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Catarina Moreira, Chihcheng Hsieh, Chun Ouyang, Jo\\~ao Madeiras Pereira, Joaquim Jorge, Yu-Liang Chou","submitted_at":"2022-03-04T16:08:21Z","abstract_excerpt":"This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have l"},"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":"2203.02399","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-04T16:08:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9e56b821c4c33acd11218aa18ae8b7323df6948ecd031931875d115ac8d34805","abstract_canon_sha256":"f9c1b8dc80589f613448ac06595056e6d7202fefd65a3748679de6f0df367d59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:38.447452Z","signature_b64":"BPhF6egR9k/4OjidvPbr1R6OdVxiUlx6BRetd3lFBz2GwjBe9zdx4+S3xy4DxZkuR0opk6/dTqdGpFaKP4z+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71e648ea35091b6dbc9e165e881e1399053e06c5501e0428747d4d932332453d","last_reissued_at":"2026-07-05T09:32:38.447021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:38.447021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Catarina Moreira, Chihcheng Hsieh, Chun Ouyang, Jo\\~ao Madeiras Pereira, Joaquim Jorge, Yu-Liang Chou","submitted_at":"2022-03-04T16:08:21Z","abstract_excerpt":"This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02399","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/2203.02399/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":"2203.02399","created_at":"2026-07-05T09:32:38.447075+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.02399v4","created_at":"2026-07-05T09:32:38.447075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02399","created_at":"2026-07-05T09:32:38.447075+00:00"},{"alias_kind":"pith_short_12","alias_value":"OHTER2RVBENW","created_at":"2026-07-05T09:32:38.447075+00:00"},{"alias_kind":"pith_short_16","alias_value":"OHTER2RVBENW3PE6","created_at":"2026-07-05T09:32:38.447075+00:00"},{"alias_kind":"pith_short_8","alias_value":"OHTER2RV","created_at":"2026-07-05T09:32:38.447075+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13090","citing_title":"MUPAX: Multidimensional Problem Agnostic eXplainable AI","ref_index":2020,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE","json":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE.json","graph_json":"https://pith.science/api/pith-number/OHTER2RVBENW3PE6CZPIQHQTTE/graph.json","events_json":"https://pith.science/api/pith-number/OHTER2RVBENW3PE6CZPIQHQTTE/events.json","paper":"https://pith.science/paper/OHTER2RV"},"agent_actions":{"view_html":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE","download_json":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE.json","view_paper":"https://pith.science/paper/OHTER2RV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.02399&json=true","fetch_graph":"https://pith.science/api/pith-number/OHTER2RVBENW3PE6CZPIQHQTTE/graph.json","fetch_events":"https://pith.science/api/pith-number/OHTER2RVBENW3PE6CZPIQHQTTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE/action/storage_attestation","attest_author":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE/action/author_attestation","sign_citation":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE/action/citation_signature","submit_replication":"https://pith.science/pith/OHTER2RVBENW3PE6CZPIQHQTTE/action/replication_record"}},"created_at":"2026-07-05T09:32:38.447075+00:00","updated_at":"2026-07-05T09:32:38.447075+00:00"}