{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HLUFLG2JWJKPJ7STTJKPMKKCL7","short_pith_number":"pith:HLUFLG2J","schema_version":"1.0","canonical_sha256":"3ae8559b49b254f4fe539a54f629425fdf2e69dca4bfd5889bd87be390fa57ed","source":{"kind":"arxiv","id":"2006.08969","version":2},"attestation_state":"computed","paper":{"title":"High Dimensional Model Explanations: an Axiomatic Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Martin Strobel, Neel Patel, Yair Zick","submitted_at":"2020-06-16T07:48:52Z","abstract_excerpt":"Complex black-box machine learning models are regularly used in critical decision-making domains. This has given rise to several calls for algorithmic explainability. Many explanation algorithms proposed in literature assign importance to each feature individually. However, such explanations fail to capture the joint effects of sets of features. Indeed, few works so far formally analyze high-dimensional model explanations. In this paper, we propose a novel high dimension model explanation method that captures the joint effect of feature subsets.\n  We propose a new axiomatization for a generali"},"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":"2006.08969","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-16T07:48:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4720a534acdab8bf7f964cc8af8f07638bcc34f3bd0125666a5a29bee4b14986","abstract_canon_sha256":"dbb291c0fa8f08d4c80875def8c436b69fbbdc13bd83d94d8598f6e6d07a6e1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:48.523183Z","signature_b64":"lHvaGUX4dt53mi7xafRsYUh6af/V6zIqbWASZ2xzP+JvXrwHMu0Z2aYy3C/MVtNKKEm2xqUcgukgFQWIgF49DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ae8559b49b254f4fe539a54f629425fdf2e69dca4bfd5889bd87be390fa57ed","last_reissued_at":"2026-07-05T02:26:48.522700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:48.522700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High Dimensional Model Explanations: an Axiomatic Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Martin Strobel, Neel Patel, Yair Zick","submitted_at":"2020-06-16T07:48:52Z","abstract_excerpt":"Complex black-box machine learning models are regularly used in critical decision-making domains. This has given rise to several calls for algorithmic explainability. Many explanation algorithms proposed in literature assign importance to each feature individually. However, such explanations fail to capture the joint effects of sets of features. Indeed, few works so far formally analyze high-dimensional model explanations. In this paper, we propose a novel high dimension model explanation method that captures the joint effect of feature subsets.\n  We propose a new axiomatization for a generali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.08969","kind":"arxiv","version":2},"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/2006.08969/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":"2006.08969","created_at":"2026-07-05T02:26:48.522760+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.08969v2","created_at":"2026-07-05T02:26:48.522760+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.08969","created_at":"2026-07-05T02:26:48.522760+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLUFLG2JWJKP","created_at":"2026-07-05T02:26:48.522760+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLUFLG2JWJKPJ7ST","created_at":"2026-07-05T02:26:48.522760+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLUFLG2J","created_at":"2026-07-05T02:26:48.522760+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7","json":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7.json","graph_json":"https://pith.science/api/pith-number/HLUFLG2JWJKPJ7STTJKPMKKCL7/graph.json","events_json":"https://pith.science/api/pith-number/HLUFLG2JWJKPJ7STTJKPMKKCL7/events.json","paper":"https://pith.science/paper/HLUFLG2J"},"agent_actions":{"view_html":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7","download_json":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7.json","view_paper":"https://pith.science/paper/HLUFLG2J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.08969&json=true","fetch_graph":"https://pith.science/api/pith-number/HLUFLG2JWJKPJ7STTJKPMKKCL7/graph.json","fetch_events":"https://pith.science/api/pith-number/HLUFLG2JWJKPJ7STTJKPMKKCL7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7/action/storage_attestation","attest_author":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7/action/author_attestation","sign_citation":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7/action/citation_signature","submit_replication":"https://pith.science/pith/HLUFLG2JWJKPJ7STTJKPMKKCL7/action/replication_record"}},"created_at":"2026-07-05T02:26:48.522760+00:00","updated_at":"2026-07-05T02:26:48.522760+00:00"}