{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OKMTJRGFJ3DFEJELNJ76AE4JS7","short_pith_number":"pith:OKMTJRGF","canonical_record":{"source":{"id":"2605.15295","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:10:57Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"1f1f6439c864d6f2b289b47a2f866d75f99525d8e6587ee899bb6087e4be5dfc","abstract_canon_sha256":"b49f722b1fb25b77c7806681ec1c42166b2c3cef5e48fc58f42eb2bae1ed4243"},"schema_version":"1.0"},"canonical_sha256":"729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20","source":{"kind":"arxiv","id":"2605.15295","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15295","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15295v1","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15295","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_12","alias_value":"OKMTJRGFJ3DF","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_16","alias_value":"OKMTJRGFJ3DFEJEL","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_8","alias_value":"OKMTJRGF","created_at":"2026-05-20T00:00:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OKMTJRGFJ3DFEJELNJ76AE4JS7","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15295","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:10:57Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"1f1f6439c864d6f2b289b47a2f866d75f99525d8e6587ee899bb6087e4be5dfc","abstract_canon_sha256":"b49f722b1fb25b77c7806681ec1c42166b2c3cef5e48fc58f42eb2bae1ed4243"},"schema_version":"1.0"},"canonical_sha256":"729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:51.174110Z","signature_b64":"1chqexNJFE03nzParwBBEriAnOVNbfEgjIhb30Ct3rboHc2FoNhd7iJSrU3tAoZWI9iCr8KH9RxxbA9i3uV9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20","last_reissued_at":"2026-05-20T00:00:51.173372Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:51.173372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15295","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":"2026-05-20T00:00:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dU1ZjiTCXwt031EU2AfZw7OxQ9SpmYWvTjJ6pKHzBZMYOG8a2XXm1V5PVZtoHHz1FKClZqWVnB8Zj9npCmApDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:34:35.092782Z"},"content_sha256":"b7067baaa14cb521d1c019aa1b5c896d52a112598436414b454b679cd3960dad","schema_version":"1.0","event_id":"sha256:b7067baaa14cb521d1c019aa1b5c896d52a112598436414b454b679cd3960dad"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OKMTJRGFJ3DFEJELNJ76AE4JS7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GESD: Beyond Outcome-Oriented Fairness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups.","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.LG","authors_text":"Gideon Popoola, John Sheppard","submitted_at":"2026-05-14T18:10:57Z","abstract_excerpt":"Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predictions. While existing fairness metrics (e.g., statistical parity, equal opportunity) effectively quantify outcome-oriented disparities, they offer limited insight into the procedure or explanation behind biased decisions. To address this gap, we propose Group-level Explanation Stability Disparity (GESD), a \\textit{procedural-oriented} fairness metric that measures disparities in the stability, robustness, and sensitivity of model explanations ac"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That measuring disparities in explanation stability, robustness, and sensitivity across subgroups provides a meaningful and model-agnostic indicator of procedural fairness without requiring additional assumptions about the underlying explainer or data distribution.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1ca1dc8307a2f54bc4e5b4dab6073b2f5d260198e71334d69e3ef06f5cdd119b"},"source":{"id":"2605.15295","kind":"arxiv","version":1},"verdict":{"id":"0cd32de7-fc37-47ae-8460-3f4d0d03193e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T16:26:09.804352Z","strongest_claim":"GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods.","one_line_summary":"The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That measuring disparities in explanation stability, robustness, and sensitivity across subgroups provides a meaningful and model-agnostic indicator of procedural fairness without requiring additional assumptions about the underlying explainer or data distribution.","pith_extraction_headline":"GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15295/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T16:36:04.312407Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.364619Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.234657Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.783854Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"a98ec30a092bbb9db1e30665e9f52baf73556af5ee2dcf02d682746a59cbc5fc"},"references":{"count":29,"sample":[{"doi":"","year":2016,"title":"Big data’s disparate impact,","work_id":"8ef3fb2b-65b4-43d7-8193-87036d2fa1ed","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Investigating and mitigating the performance–fairness tradeoff via protected-category sampling,","work_id":"f9665236-7427-4074-b52c-1cdb2354e670","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Optimized pre-processing for discrimination prevention","work_id":"d285c33d-8270-4df5-a8ee-82dc61103423","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,","work_id":"7a40f3a2-2b53-4d63-bed0-b01fd10cf685","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Equality of opportunity in super- vised learning,","work_id":"333f5ab2-14d7-4d29-a133-71e893194478","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":29,"snapshot_sha256":"c2753c2a08086e38f4cb6240f458c30266967cba1eb89159d91fdb882f4493f3","internal_anchors":1},"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":"0cd32de7-fc37-47ae-8460-3f4d0d03193e"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v/WnC+VxRygUaqkCqdZKiJ5dBZfct7w77q1lESskZ2Enel/V6ygYe0i07c40ZWv1g1Ogxzwr5CqfbZ+KC2CLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:34:35.094060Z"},"content_sha256":"dae1e07f5c2ecc315c2d05a7f4dfe2b9f8ad75d16f1e669a5f990650ccac44c8","schema_version":"1.0","event_id":"sha256:dae1e07f5c2ecc315c2d05a7f4dfe2b9f8ad75d16f1e669a5f990650ccac44c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7/bundle.json","state_url":"https://pith.science/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7/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":"2026-05-25T17:34:35Z","links":{"resolver":"https://pith.science/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7","bundle":"https://pith.science/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7/bundle.json","state":"https://pith.science/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OKMTJRGFJ3DFEJELNJ76AE4JS7","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":"b49f722b1fb25b77c7806681ec1c42166b2c3cef5e48fc58f42eb2bae1ed4243","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:10:57Z","title_canon_sha256":"1f1f6439c864d6f2b289b47a2f866d75f99525d8e6587ee899bb6087e4be5dfc"},"schema_version":"1.0","source":{"id":"2605.15295","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15295","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15295v1","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15295","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_12","alias_value":"OKMTJRGFJ3DF","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_16","alias_value":"OKMTJRGFJ3DFEJEL","created_at":"2026-05-20T00:00:51Z"},{"alias_kind":"pith_short_8","alias_value":"OKMTJRGF","created_at":"2026-05-20T00:00:51Z"}],"graph_snapshots":[{"event_id":"sha256:dae1e07f5c2ecc315c2d05a7f4dfe2b9f8ad75d16f1e669a5f990650ccac44c8","target":"graph","created_at":"2026-05-20T00:00:51Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That measuring disparities in explanation stability, robustness, and sensitivity across subgroups provides a meaningful and model-agnostic indicator of procedural fairness without requiring additional assumptions about the underlying explainer or data distribution."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups."}],"snapshot_sha256":"1ca1dc8307a2f54bc4e5b4dab6073b2f5d260198e71334d69e3ef06f5cdd119b"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:36:04.312407Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.364619Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.234657Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.783854Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15295/integrity.json","findings":[],"snapshot_sha256":"a98ec30a092bbb9db1e30665e9f52baf73556af5ee2dcf02d682746a59cbc5fc","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predictions. While existing fairness metrics (e.g., statistical parity, equal opportunity) effectively quantify outcome-oriented disparities, they offer limited insight into the procedure or explanation behind biased decisions. To address this gap, we propose Group-level Explanation Stability Disparity (GESD), a \\textit{procedural-oriented} fairness metric that measures disparities in the stability, robustness, and sensitivity of model explanations ac","authors_text":"Gideon Popoola, John Sheppard","cross_cats":["cs.AI","cs.CY"],"headline":"GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:10:57Z","title":"GESD: Beyond Outcome-Oriented Fairness"},"references":{"count":29,"internal_anchors":1,"resolved_work":29,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Big data’s disparate impact,","work_id":"8ef3fb2b-65b4-43d7-8193-87036d2fa1ed","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Investigating and mitigating the performance–fairness tradeoff via protected-category sampling,","work_id":"f9665236-7427-4074-b52c-1cdb2354e670","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Optimized pre-processing for discrimination prevention","work_id":"d285c33d-8270-4df5-a8ee-82dc61103423","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,","work_id":"7a40f3a2-2b53-4d63-bed0-b01fd10cf685","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Equality of opportunity in super- vised learning,","work_id":"333f5ab2-14d7-4d29-a133-71e893194478","year":2016}],"snapshot_sha256":"c2753c2a08086e38f4cb6240f458c30266967cba1eb89159d91fdb882f4493f3"},"source":{"id":"2605.15295","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T16:26:09.804352Z","id":"0cd32de7-fc37-47ae-8460-3f4d0d03193e","model_set":{"reader":"grok-4.3"},"one_line_summary":"The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups.","strongest_claim":"GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods.","weakest_assumption":"That measuring disparities in explanation stability, robustness, and sensitivity across subgroups provides a meaningful and model-agnostic indicator of procedural fairness without requiring additional assumptions about the underlying explainer or data distribution."}},"verdict_id":"0cd32de7-fc37-47ae-8460-3f4d0d03193e"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b7067baaa14cb521d1c019aa1b5c896d52a112598436414b454b679cd3960dad","target":"record","created_at":"2026-05-20T00:00:51Z","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":"b49f722b1fb25b77c7806681ec1c42166b2c3cef5e48fc58f42eb2bae1ed4243","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:10:57Z","title_canon_sha256":"1f1f6439c864d6f2b289b47a2f866d75f99525d8e6587ee899bb6087e4be5dfc"},"schema_version":"1.0","source":{"id":"2605.15295","kind":"arxiv","version":1}},"canonical_sha256":"729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20","first_computed_at":"2026-05-20T00:00:51.173372Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:00:51.173372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1chqexNJFE03nzParwBBEriAnOVNbfEgjIhb30Ct3rboHc2FoNhd7iJSrU3tAoZWI9iCr8KH9RxxbA9i3uV9CA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:00:51.174110Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15295","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7067baaa14cb521d1c019aa1b5c896d52a112598436414b454b679cd3960dad","sha256:dae1e07f5c2ecc315c2d05a7f4dfe2b9f8ad75d16f1e669a5f990650ccac44c8"],"state_sha256":"0b4ba81846aab2693819f9c6f1343349634b45084735a42afa6ce846128ba74e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7PzUuQrhcQMG7C3teuYyEOe9XGx4E7fBPrmurcMCgy7XaQcRxRZTAFmxXma69vSWS++W7ZTRwrDHKDeGI9QBCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:34:35.099365Z","bundle_sha256":"bf4bcfa8a9d27f60a2361450c05fb144e54ee48782fa91fbcacf69a2e8d7cddb"}}