{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:O63AJWSQEETMLVYPWNYAU4EYPX","short_pith_number":"pith:O63AJWSQ","canonical_record":{"source":{"id":"2506.06653","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2025-06-07T04:15:27Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"76aaac793be34823f64bb958bdd5881a28a78c60a7c79993c35b833f4992589c","abstract_canon_sha256":"f6676079ec1c72c0760f89def65e76f7a4b2cabdf4c5850b400afbd2d7da0c66"},"schema_version":"1.0"},"canonical_sha256":"77b604da502126c5d70fb3700a70987de78457f037620ae50d3e67d79d075800","source":{"kind":"arxiv","id":"2506.06653","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06653","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06653v1","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06653","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"pith_short_12","alias_value":"O63AJWSQEETM","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"pith_short_16","alias_value":"O63AJWSQEETMLVYP","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"pith_short_8","alias_value":"O63AJWSQ","created_at":"2026-07-05T11:17:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:O63AJWSQEETMLVYPWNYAU4EYPX","target":"record","payload":{"canonical_record":{"source":{"id":"2506.06653","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2025-06-07T04:15:27Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"76aaac793be34823f64bb958bdd5881a28a78c60a7c79993c35b833f4992589c","abstract_canon_sha256":"f6676079ec1c72c0760f89def65e76f7a4b2cabdf4c5850b400afbd2d7da0c66"},"schema_version":"1.0"},"canonical_sha256":"77b604da502126c5d70fb3700a70987de78457f037620ae50d3e67d79d075800","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:53.474161Z","signature_b64":"1RxVa+Jvicpo+ufAUKCWZrJxsTG9jl15o1vrPDmzB/HN5Vl8Hk9a1BP30SKyRF9My16PpSw83286yVaHueKuAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77b604da502126c5d70fb3700a70987de78457f037620ae50d3e67d79d075800","last_reissued_at":"2026-07-05T11:17:53.473700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:53.473700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.06653","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":"2026-07-05T11:17:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KMRUUs4BHVNsM4lm/2LebR3AIzFlgjdtfwGIfq1V0/2mEM4tv89QnjEUdsmuOwJiGHLkKlQXQ/LZVMao1eU2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:28:28.988231Z"},"content_sha256":"41f0390edd645711c6d5e941bd0fcd490a095f80e422520f891d334300d85afd","schema_version":"1.0","event_id":"sha256:41f0390edd645711c6d5e941bd0fcd490a095f80e422520f891d334300d85afd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:O63AJWSQEETMLVYPWNYAU4EYPX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explaining Risks: Axiomatic Risk Attributions for Financial Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-fin.CP","authors_text":"Dangxing Chen","submitted_at":"2025-06-07T04:15:27Z","abstract_excerpt":"In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06653","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/2506.06653/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":"2026-07-05T11:17:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZDKQ9rHTVzcyRwdYKqQMu1MLtNtY4XDvrX5jaWZbwKqTAabVQYiG5zvuH331SrHXAcXGmN9cPtxz53ZIcG4tAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:28:28.988744Z"},"content_sha256":"4aac6742a0ee9f9cef301a779b6d32973df1d6f53f724c47ad29b839b2b29850","schema_version":"1.0","event_id":"sha256:4aac6742a0ee9f9cef301a779b6d32973df1d6f53f724c47ad29b839b2b29850"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O63AJWSQEETMLVYPWNYAU4EYPX/bundle.json","state_url":"https://pith.science/pith/O63AJWSQEETMLVYPWNYAU4EYPX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O63AJWSQEETMLVYPWNYAU4EYPX/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":"2026-08-10T07:28:28Z","links":{"resolver":"https://pith.science/pith/O63AJWSQEETMLVYPWNYAU4EYPX","bundle":"https://pith.science/pith/O63AJWSQEETMLVYPWNYAU4EYPX/bundle.json","state":"https://pith.science/pith/O63AJWSQEETMLVYPWNYAU4EYPX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O63AJWSQEETMLVYPWNYAU4EYPX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:O63AJWSQEETMLVYPWNYAU4EYPX","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":"f6676079ec1c72c0760f89def65e76f7a4b2cabdf4c5850b400afbd2d7da0c66","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2025-06-07T04:15:27Z","title_canon_sha256":"76aaac793be34823f64bb958bdd5881a28a78c60a7c79993c35b833f4992589c"},"schema_version":"1.0","source":{"id":"2506.06653","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06653","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06653v1","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06653","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"pith_short_12","alias_value":"O63AJWSQEETM","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"pith_short_16","alias_value":"O63AJWSQEETMLVYP","created_at":"2026-07-05T11:17:53Z"},{"alias_kind":"pith_short_8","alias_value":"O63AJWSQ","created_at":"2026-07-05T11:17:53Z"}],"graph_snapshots":[{"event_id":"sha256:4aac6742a0ee9f9cef301a779b6d32973df1d6f53f724c47ad29b839b2b29850","target":"graph","created_at":"2026-07-05T11:17:53Z","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/2506.06653/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley v","authors_text":"Dangxing Chen","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2025-06-07T04:15:27Z","title":"Explaining Risks: Axiomatic Risk Attributions for Financial Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06653","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":"sha256:41f0390edd645711c6d5e941bd0fcd490a095f80e422520f891d334300d85afd","target":"record","created_at":"2026-07-05T11:17:53Z","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":"f6676079ec1c72c0760f89def65e76f7a4b2cabdf4c5850b400afbd2d7da0c66","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2025-06-07T04:15:27Z","title_canon_sha256":"76aaac793be34823f64bb958bdd5881a28a78c60a7c79993c35b833f4992589c"},"schema_version":"1.0","source":{"id":"2506.06653","kind":"arxiv","version":1}},"canonical_sha256":"77b604da502126c5d70fb3700a70987de78457f037620ae50d3e67d79d075800","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77b604da502126c5d70fb3700a70987de78457f037620ae50d3e67d79d075800","first_computed_at":"2026-07-05T11:17:53.473700Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:53.473700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1RxVa+Jvicpo+ufAUKCWZrJxsTG9jl15o1vrPDmzB/HN5Vl8Hk9a1BP30SKyRF9My16PpSw83286yVaHueKuAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:53.474161Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06653","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:41f0390edd645711c6d5e941bd0fcd490a095f80e422520f891d334300d85afd","sha256:4aac6742a0ee9f9cef301a779b6d32973df1d6f53f724c47ad29b839b2b29850"],"state_sha256":"74129a9830faf5bc701f2145c13126e6c4c46ef722c653b42050b481ffdb76be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7SzvcfKX0/8ERgfdrEG/GfZDQYXDCq4mqUW374AA85SzzMdnRZ7JCmZXfO5Nu9IxHG/nxZY8fHVd/+P1nkbPCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T07:28:28.993405Z","bundle_sha256":"b500e69bf1a3a590be1efc992681cca9c99a322ca427860e5774b6aeae101fc3"}}