{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:GV5HJ5A3I3PVU57XPNGQPVN6EU","short_pith_number":"pith:GV5HJ5A3","canonical_record":{"source":{"id":"2211.08943","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-16T14:45:16Z","cross_cats_sorted":["cs.AI","cs.LG","physics.ao-ph","stat.AP"],"title_canon_sha256":"6a3a76dc1ac17c728ab27edf2b7680d7ff2c5d3b63356f86ad0a5a5efb09e43a","abstract_canon_sha256":"fe46c5ebab1835973b828a0083e0f36bef46d2d78ce83744ee7ebf1bc78e3be3"},"schema_version":"1.0"},"canonical_sha256":"357a74f41b46df5a77f77b4d07d5be25049dbb83ab210df9e0cbe7225b64e0b9","source":{"kind":"arxiv","id":"2211.08943","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.08943","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"arxiv_version","alias_value":"2211.08943v1","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.08943","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"pith_short_12","alias_value":"GV5HJ5A3I3PV","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"pith_short_16","alias_value":"GV5HJ5A3I3PVU57X","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"pith_short_8","alias_value":"GV5HJ5A3","created_at":"2026-07-05T05:16:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:GV5HJ5A3I3PVU57XPNGQPVN6EU","target":"record","payload":{"canonical_record":{"source":{"id":"2211.08943","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-16T14:45:16Z","cross_cats_sorted":["cs.AI","cs.LG","physics.ao-ph","stat.AP"],"title_canon_sha256":"6a3a76dc1ac17c728ab27edf2b7680d7ff2c5d3b63356f86ad0a5a5efb09e43a","abstract_canon_sha256":"fe46c5ebab1835973b828a0083e0f36bef46d2d78ce83744ee7ebf1bc78e3be3"},"schema_version":"1.0"},"canonical_sha256":"357a74f41b46df5a77f77b4d07d5be25049dbb83ab210df9e0cbe7225b64e0b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:16:45.658777Z","signature_b64":"m6XRxojO4ZcAfTM2UvOa3TfDiI0l7gVn3NUKsoNH1nDTuuZtHLda3TRPcE1V4ab6pfdH4HakZ6EdxU3LcfodCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"357a74f41b46df5a77f77b4d07d5be25049dbb83ab210df9e0cbe7225b64e0b9","last_reissued_at":"2026-07-05T05:16:45.658305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:16:45.658305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.08943","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:16:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NkkqD6UsZR1e+BgKLsvIhMayiyHmEdzSwhlOjbeDrP6TljJvt6rB49pv3vmIGsZsm8hCddZRIRv7/L7aQyhYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T13:30:21.348022Z"},"content_sha256":"0e55d5d00ef5a8e2f2a0902bf932643fbc92b06973737f355292228c899abb1a","schema_version":"1.0","event_id":"sha256:0e55d5d00ef5a8e2f2a0902bf932643fbc92b06973737f355292228c899abb1a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:GV5HJ5A3I3PVU57XPNGQPVN6EU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","physics.ao-ph","stat.AP"],"primary_cat":"stat.ML","authors_text":"Amy McGovern, Corey Potvin, Montgomery Flora, Shawn Handler","submitted_at":"2022-11-16T14:45:16Z","abstract_excerpt":"With increasing interest in explaining machine learning (ML) models, the first part of this two-part study synthesizes recent research on methods for explaining global and local aspects of ML models. This study distinguishes explainability from interpretability, local from global explainability, and feature importance versus feature relevance. We demonstrate and visualize different explanation methods, how to interpret them, and provide a complete Python package (scikit-explain) to allow future researchers to explore these products. We also highlight the frequent disagreement between explanati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.08943","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/2211.08943/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:16:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fCk2qqm4wlGNFecTxIqkuuZ/na4tj1YqjP1hCcGEf9HYiYWOguV+A9wUMq6UiP57m6a3VVu5OS0AjuAmoSDjAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T13:30:21.348565Z"},"content_sha256":"0d23343dc5e9ef9f096fd1e9e34915e9a7276c457c476ad13e4ede6e80cd2aab","schema_version":"1.0","event_id":"sha256:0d23343dc5e9ef9f096fd1e9e34915e9a7276c457c476ad13e4ede6e80cd2aab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU/bundle.json","state_url":"https://pith.science/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T13:30:21Z","links":{"resolver":"https://pith.science/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU","bundle":"https://pith.science/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU/bundle.json","state":"https://pith.science/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GV5HJ5A3I3PVU57XPNGQPVN6EU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GV5HJ5A3I3PVU57XPNGQPVN6EU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fe46c5ebab1835973b828a0083e0f36bef46d2d78ce83744ee7ebf1bc78e3be3","cross_cats_sorted":["cs.AI","cs.LG","physics.ao-ph","stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-16T14:45:16Z","title_canon_sha256":"6a3a76dc1ac17c728ab27edf2b7680d7ff2c5d3b63356f86ad0a5a5efb09e43a"},"schema_version":"1.0","source":{"id":"2211.08943","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.08943","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"arxiv_version","alias_value":"2211.08943v1","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.08943","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"pith_short_12","alias_value":"GV5HJ5A3I3PV","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"pith_short_16","alias_value":"GV5HJ5A3I3PVU57X","created_at":"2026-07-05T05:16:45Z"},{"alias_kind":"pith_short_8","alias_value":"GV5HJ5A3","created_at":"2026-07-05T05:16:45Z"}],"graph_snapshots":[{"event_id":"sha256:0d23343dc5e9ef9f096fd1e9e34915e9a7276c457c476ad13e4ede6e80cd2aab","target":"graph","created_at":"2026-07-05T05:16:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.08943/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With increasing interest in explaining machine learning (ML) models, the first part of this two-part study synthesizes recent research on methods for explaining global and local aspects of ML models. This study distinguishes explainability from interpretability, local from global explainability, and feature importance versus feature relevance. We demonstrate and visualize different explanation methods, how to interpret them, and provide a complete Python package (scikit-explain) to allow future researchers to explore these products. We also highlight the frequent disagreement between explanati","authors_text":"Amy McGovern, Corey Potvin, Montgomery Flora, Shawn Handler","cross_cats":["cs.AI","cs.LG","physics.ao-ph","stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-16T14:45:16Z","title":"Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.08943","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0e55d5d00ef5a8e2f2a0902bf932643fbc92b06973737f355292228c899abb1a","target":"record","created_at":"2026-07-05T05:16:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fe46c5ebab1835973b828a0083e0f36bef46d2d78ce83744ee7ebf1bc78e3be3","cross_cats_sorted":["cs.AI","cs.LG","physics.ao-ph","stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-16T14:45:16Z","title_canon_sha256":"6a3a76dc1ac17c728ab27edf2b7680d7ff2c5d3b63356f86ad0a5a5efb09e43a"},"schema_version":"1.0","source":{"id":"2211.08943","kind":"arxiv","version":1}},"canonical_sha256":"357a74f41b46df5a77f77b4d07d5be25049dbb83ab210df9e0cbe7225b64e0b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"357a74f41b46df5a77f77b4d07d5be25049dbb83ab210df9e0cbe7225b64e0b9","first_computed_at":"2026-07-05T05:16:45.658305Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:16:45.658305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"m6XRxojO4ZcAfTM2UvOa3TfDiI0l7gVn3NUKsoNH1nDTuuZtHLda3TRPcE1V4ab6pfdH4HakZ6EdxU3LcfodCw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:16:45.658777Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.08943","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0e55d5d00ef5a8e2f2a0902bf932643fbc92b06973737f355292228c899abb1a","sha256:0d23343dc5e9ef9f096fd1e9e34915e9a7276c457c476ad13e4ede6e80cd2aab"],"state_sha256":"83d7765ee13bf77fcdf757f508f776961de124b4cc24205e5c5ecdf30480d2a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bCQinyvJSvhNLfd0RJtDdCLD7zdgn9E9/dn8zm2kPaXnWkVyW80lCPRUo5wVk+PVIayIW8VyQzJCBsBUp4ggAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T13:30:21.355872Z","bundle_sha256":"d18b5f3a023d18cdd6ff2b8d728bc5a9e101b427e26e048a0289b37d559ac87c"}}