{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NWHOIM4EYLZ3NS65YGLX6MC3LF","short_pith_number":"pith:NWHOIM4E","schema_version":"1.0","canonical_sha256":"6d8ee43384c2f3b6cbddc1977f305b59617bca4b202f4f25ba91528d47d16cf8","source":{"kind":"arxiv","id":"2301.09042","version":1},"attestation_state":"computed","paper":{"title":"The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Brett Mullins","submitted_at":"2023-01-22T02:58:00Z","abstract_excerpt":"Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for arbitrary or black-box classifiers. But what makes these methods work in general? We introduce a topological framework for rule-based explanation methods and provide a characterization of explainability in terms of the definability of a classifier relative to an explanation scheme. We employ this framework to consider various explanation schemes and argue that the"},"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":"2301.09042","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-22T02:58:00Z","cross_cats_sorted":[],"title_canon_sha256":"0a13902cf2d9f9187cdcf17d63f5f33d7a60a58b056becba346de78b0a42a5c1","abstract_canon_sha256":"99a1014620ed947f273ea2913b55b855899da1673fb438b6a8e14f31d1edea17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:34:55.786351Z","signature_b64":"jW/biRot8QbR7r92uBUD1JzbJ8bpNs690DLzd69J/S05oxnAwd59ATyK7mUsfM4+uBcLouezhCTjXC1bCb+ZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d8ee43384c2f3b6cbddc1977f305b59617bca4b202f4f25ba91528d47d16cf8","last_reissued_at":"2026-07-05T05:34:55.785929Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:34:55.785929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Brett Mullins","submitted_at":"2023-01-22T02:58:00Z","abstract_excerpt":"Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for arbitrary or black-box classifiers. But what makes these methods work in general? We introduce a topological framework for rule-based explanation methods and provide a characterization of explainability in terms of the definability of a classifier relative to an explanation scheme. We employ this framework to consider various explanation schemes and argue that the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09042","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/2301.09042/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":"2301.09042","created_at":"2026-07-05T05:34:55.785992+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.09042v1","created_at":"2026-07-05T05:34:55.785992+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09042","created_at":"2026-07-05T05:34:55.785992+00:00"},{"alias_kind":"pith_short_12","alias_value":"NWHOIM4EYLZ3","created_at":"2026-07-05T05:34:55.785992+00:00"},{"alias_kind":"pith_short_16","alias_value":"NWHOIM4EYLZ3NS65","created_at":"2026-07-05T05:34:55.785992+00:00"},{"alias_kind":"pith_short_8","alias_value":"NWHOIM4E","created_at":"2026-07-05T05:34:55.785992+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.08432","citing_title":"xpSHACL: Explainable SHACL Validation using Retrieval-Augmented Generation and Large Language Models","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF","json":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF.json","graph_json":"https://pith.science/api/pith-number/NWHOIM4EYLZ3NS65YGLX6MC3LF/graph.json","events_json":"https://pith.science/api/pith-number/NWHOIM4EYLZ3NS65YGLX6MC3LF/events.json","paper":"https://pith.science/paper/NWHOIM4E"},"agent_actions":{"view_html":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF","download_json":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF.json","view_paper":"https://pith.science/paper/NWHOIM4E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.09042&json=true","fetch_graph":"https://pith.science/api/pith-number/NWHOIM4EYLZ3NS65YGLX6MC3LF/graph.json","fetch_events":"https://pith.science/api/pith-number/NWHOIM4EYLZ3NS65YGLX6MC3LF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF/action/storage_attestation","attest_author":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF/action/author_attestation","sign_citation":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF/action/citation_signature","submit_replication":"https://pith.science/pith/NWHOIM4EYLZ3NS65YGLX6MC3LF/action/replication_record"}},"created_at":"2026-07-05T05:34:55.785992+00:00","updated_at":"2026-07-05T05:34:55.785992+00:00"}