{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:6CMMEQ5BR6DMTCAT6U6UK2THUD","short_pith_number":"pith:6CMMEQ5B","schema_version":"1.0","canonical_sha256":"f098c243a18f86c98813f53d456a67a0c6b02d6f3ec75eeb39d66e8b3803de98","source":{"kind":"arxiv","id":"2012.00360","version":1},"attestation_state":"computed","paper":{"title":"Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Alfonso Ortega, Aythami Morales, Julian Fierrez, Tony Ribeiro, Zilong Wang","submitted_at":"2020-12-01T09:36:59Z","abstract_excerpt":"Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain co"},"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":"2012.00360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2020-12-01T09:36:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"017dff8fec9ccf9f33679ff3c45105a7aa2cb814f653a5a321857932339c67b6","abstract_canon_sha256":"c01083a42c702d4ddbe19c429ba632c8966d6c18dccd78a3b104ee6f950a0897"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:35.384353Z","signature_b64":"URTr1RHJ204/9svCmTTNe18qnITuMPbGL5wBdNhnaCH3TKy1GTl0u40zTbOkq3WBevxYm+XwHByBmS+0mucYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f098c243a18f86c98813f53d456a67a0c6b02d6f3ec75eeb39d66e8b3803de98","last_reissued_at":"2026-07-05T01:55:35.383810Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:35.383810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Alfonso Ortega, Aythami Morales, Julian Fierrez, Tony Ribeiro, Zilong Wang","submitted_at":"2020-12-01T09:36:59Z","abstract_excerpt":"Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.00360","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/2012.00360/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":"2012.00360","created_at":"2026-07-05T01:55:35.383877+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.00360v1","created_at":"2026-07-05T01:55:35.383877+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.00360","created_at":"2026-07-05T01:55:35.383877+00:00"},{"alias_kind":"pith_short_12","alias_value":"6CMMEQ5BR6DM","created_at":"2026-07-05T01:55:35.383877+00:00"},{"alias_kind":"pith_short_16","alias_value":"6CMMEQ5BR6DMTCAT","created_at":"2026-07-05T01:55:35.383877+00:00"},{"alias_kind":"pith_short_8","alias_value":"6CMMEQ5B","created_at":"2026-07-05T01:55:35.383877+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.08356","citing_title":"A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD","json":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD.json","graph_json":"https://pith.science/api/pith-number/6CMMEQ5BR6DMTCAT6U6UK2THUD/graph.json","events_json":"https://pith.science/api/pith-number/6CMMEQ5BR6DMTCAT6U6UK2THUD/events.json","paper":"https://pith.science/paper/6CMMEQ5B"},"agent_actions":{"view_html":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD","download_json":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD.json","view_paper":"https://pith.science/paper/6CMMEQ5B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.00360&json=true","fetch_graph":"https://pith.science/api/pith-number/6CMMEQ5BR6DMTCAT6U6UK2THUD/graph.json","fetch_events":"https://pith.science/api/pith-number/6CMMEQ5BR6DMTCAT6U6UK2THUD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD/action/storage_attestation","attest_author":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD/action/author_attestation","sign_citation":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD/action/citation_signature","submit_replication":"https://pith.science/pith/6CMMEQ5BR6DMTCAT6U6UK2THUD/action/replication_record"}},"created_at":"2026-07-05T01:55:35.383877+00:00","updated_at":"2026-07-05T01:55:35.383877+00:00"}