{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:KQCNWU2XBBIYSPMH4SHCY3JNNO","short_pith_number":"pith:KQCNWU2X","schema_version":"1.0","canonical_sha256":"5404db53570851893d87e48e2c6d2d6bbf361eb97576f137d63f3a393c4f0cad","source":{"kind":"arxiv","id":"2007.04131","version":2},"attestation_state":"computed","paper":{"title":"General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bernd Bischl, Christian A. Scholbeck, Christoph Molnar, Giuseppe Casalicchio, Gunnar K\\\"onig, Julia Herbinger, Moritz Grosse-Wentrup, Susanne Dandl, Timo Freiesleben","submitted_at":"2020-07-08T14:02:56Z","abstract_excerpt":"An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified ca"},"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":"2007.04131","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-07-08T14:02:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"47982d368a11f05d7343f275ea09eeed09eb0632f91955ff8b6dd2fb39b042d8","abstract_canon_sha256":"b7b7022edd8a6fc6815de5864c9102e89897684dcfe97215337158758a3f2d13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:06:11.577003Z","signature_b64":"FcxEnZaIXbi/HW0Frf+OgDkVUfpqkvj2V3oPdZorpGgowa+hbqCaD/DJbu69Pr3R05oGZB9RY8mZEmzIrAnWAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5404db53570851893d87e48e2c6d2d6bbf361eb97576f137d63f3a393c4f0cad","last_reissued_at":"2026-07-05T03:06:11.576622Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:06:11.576622Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bernd Bischl, Christian A. Scholbeck, Christoph Molnar, Giuseppe Casalicchio, Gunnar K\\\"onig, Julia Herbinger, Moritz Grosse-Wentrup, Susanne Dandl, Timo Freiesleben","submitted_at":"2020-07-08T14:02:56Z","abstract_excerpt":"An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.04131","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/2007.04131/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":"2007.04131","created_at":"2026-07-05T03:06:11.576675+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.04131v2","created_at":"2026-07-05T03:06:11.576675+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.04131","created_at":"2026-07-05T03:06:11.576675+00:00"},{"alias_kind":"pith_short_12","alias_value":"KQCNWU2XBBIY","created_at":"2026-07-05T03:06:11.576675+00:00"},{"alias_kind":"pith_short_16","alias_value":"KQCNWU2XBBIYSPMH","created_at":"2026-07-05T03:06:11.576675+00:00"},{"alias_kind":"pith_short_8","alias_value":"KQCNWU2X","created_at":"2026-07-05T03:06:11.576675+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12701","citing_title":"Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions","ref_index":162,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12809","citing_title":"Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces","ref_index":159,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09852","citing_title":"Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO","json":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO.json","graph_json":"https://pith.science/api/pith-number/KQCNWU2XBBIYSPMH4SHCY3JNNO/graph.json","events_json":"https://pith.science/api/pith-number/KQCNWU2XBBIYSPMH4SHCY3JNNO/events.json","paper":"https://pith.science/paper/KQCNWU2X"},"agent_actions":{"view_html":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO","download_json":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO.json","view_paper":"https://pith.science/paper/KQCNWU2X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.04131&json=true","fetch_graph":"https://pith.science/api/pith-number/KQCNWU2XBBIYSPMH4SHCY3JNNO/graph.json","fetch_events":"https://pith.science/api/pith-number/KQCNWU2XBBIYSPMH4SHCY3JNNO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO/action/storage_attestation","attest_author":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO/action/author_attestation","sign_citation":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO/action/citation_signature","submit_replication":"https://pith.science/pith/KQCNWU2XBBIYSPMH4SHCY3JNNO/action/replication_record"}},"created_at":"2026-07-05T03:06:11.576675+00:00","updated_at":"2026-07-05T03:06:11.576675+00:00"}