{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XY4E22JDHTNY47VA3HFCEHVF67","short_pith_number":"pith:XY4E22JD","canonical_record":{"source":{"id":"2505.00830","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-01T19:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"55209cecbfbca4c276b416193c6cc796cfe99ce4895b6119ec8418bb082e4010","abstract_canon_sha256":"61a3e6dffd1ed3f6c8d4efe5601eec30dffb07126a47441bf54531b4d5a86f3d"},"schema_version":"1.0"},"canonical_sha256":"be384d69233cdb8e7ea0d9ca221ea5f7c21242ee149489d071feb2f9dda4c0ff","source":{"kind":"arxiv","id":"2505.00830","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.00830","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"arxiv_version","alias_value":"2505.00830v2","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00830","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"pith_short_12","alias_value":"XY4E22JDHTNY","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"pith_short_16","alias_value":"XY4E22JDHTNY47VA","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"pith_short_8","alias_value":"XY4E22JD","created_at":"2026-07-05T11:46:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XY4E22JDHTNY47VA3HFCEHVF67","target":"record","payload":{"canonical_record":{"source":{"id":"2505.00830","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-01T19:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"55209cecbfbca4c276b416193c6cc796cfe99ce4895b6119ec8418bb082e4010","abstract_canon_sha256":"61a3e6dffd1ed3f6c8d4efe5601eec30dffb07126a47441bf54531b4d5a86f3d"},"schema_version":"1.0"},"canonical_sha256":"be384d69233cdb8e7ea0d9ca221ea5f7c21242ee149489d071feb2f9dda4c0ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:21.902114Z","signature_b64":"9Sf+1GeM6imTKFrDtsLCD7fn0a61RKY6FSVkomK2yTyP57B7uUgGpg0g/nCC2iskLvZgCJfzvvgq9Ndg/kkmDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be384d69233cdb8e7ea0d9ca221ea5f7c21242ee149489d071feb2f9dda4c0ff","last_reissued_at":"2026-07-05T11:46:21.901611Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:21.901611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.00830","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:46:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P+gz2YXldXmi6o3feLUIIKh+MAWkINj7mLNJuSjBdkUuPSzkLKqAlpX1rUOI14HFdL6Pk4nvr2nMhEJBtGYHDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:10:31.465856Z"},"content_sha256":"54ad1ce48e2c11774e869ee4604816deb5148dab1117355731ae3d2f7b452ef4","schema_version":"1.0","event_id":"sha256:54ad1ce48e2c11774e869ee4604816deb5148dab1117355731ae3d2f7b452ef4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XY4E22JDHTNY47VA3HFCEHVF67","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Intersectional Divergence: Measuring Fairness in Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Joe Germino, Nitesh V. Chawla, Nuno Moniz","submitted_at":"2025-05-01T19:43:12Z","abstract_excerpt":"Fairness in machine learning research is commonly framed in the context of classification tasks, leaving critical gaps in regression. In this paper, we propose a novel approach to measure intersectional fairness in regression tasks, going beyond the focus on single protected attributes from existing work to consider combinations of all protected attributes. Furthermore, we contend that it is insufficient to measure the average error of groups without regard for imbalanced domain preferences. Accordingly, we propose Intersectional Divergence (ID) as the first fairness measure for regression tas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00830","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/2505.00830/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":"2026-07-05T11:46:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KCVpfvO6F9rPtE887t6zLN5KmUOA0rghaV/AxoFc1OH8INRyPYFMbn+sKjfhMtL1KauQ5hivXtD3lDtT6L8gDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:10:31.466603Z"},"content_sha256":"f3bb41a9e57ad5a7f0c8952bb9e0c9d7c86489f963accea8814761db1dd92847","schema_version":"1.0","event_id":"sha256:f3bb41a9e57ad5a7f0c8952bb9e0c9d7c86489f963accea8814761db1dd92847"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XY4E22JDHTNY47VA3HFCEHVF67/bundle.json","state_url":"https://pith.science/pith/XY4E22JDHTNY47VA3HFCEHVF67/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XY4E22JDHTNY47VA3HFCEHVF67/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":"2026-08-17T16:10:31Z","links":{"resolver":"https://pith.science/pith/XY4E22JDHTNY47VA3HFCEHVF67","bundle":"https://pith.science/pith/XY4E22JDHTNY47VA3HFCEHVF67/bundle.json","state":"https://pith.science/pith/XY4E22JDHTNY47VA3HFCEHVF67/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XY4E22JDHTNY47VA3HFCEHVF67/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XY4E22JDHTNY47VA3HFCEHVF67","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":"61a3e6dffd1ed3f6c8d4efe5601eec30dffb07126a47441bf54531b4d5a86f3d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-01T19:43:12Z","title_canon_sha256":"55209cecbfbca4c276b416193c6cc796cfe99ce4895b6119ec8418bb082e4010"},"schema_version":"1.0","source":{"id":"2505.00830","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.00830","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"arxiv_version","alias_value":"2505.00830v2","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00830","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"pith_short_12","alias_value":"XY4E22JDHTNY","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"pith_short_16","alias_value":"XY4E22JDHTNY47VA","created_at":"2026-07-05T11:46:21Z"},{"alias_kind":"pith_short_8","alias_value":"XY4E22JD","created_at":"2026-07-05T11:46:21Z"}],"graph_snapshots":[{"event_id":"sha256:f3bb41a9e57ad5a7f0c8952bb9e0c9d7c86489f963accea8814761db1dd92847","target":"graph","created_at":"2026-07-05T11:46:21Z","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/2505.00830/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fairness in machine learning research is commonly framed in the context of classification tasks, leaving critical gaps in regression. In this paper, we propose a novel approach to measure intersectional fairness in regression tasks, going beyond the focus on single protected attributes from existing work to consider combinations of all protected attributes. Furthermore, we contend that it is insufficient to measure the average error of groups without regard for imbalanced domain preferences. Accordingly, we propose Intersectional Divergence (ID) as the first fairness measure for regression tas","authors_text":"Joe Germino, Nitesh V. Chawla, Nuno Moniz","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-01T19:43:12Z","title":"Intersectional Divergence: Measuring Fairness in Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00830","kind":"arxiv","version":2},"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":"sha256:54ad1ce48e2c11774e869ee4604816deb5148dab1117355731ae3d2f7b452ef4","target":"record","created_at":"2026-07-05T11:46:21Z","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":"61a3e6dffd1ed3f6c8d4efe5601eec30dffb07126a47441bf54531b4d5a86f3d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-01T19:43:12Z","title_canon_sha256":"55209cecbfbca4c276b416193c6cc796cfe99ce4895b6119ec8418bb082e4010"},"schema_version":"1.0","source":{"id":"2505.00830","kind":"arxiv","version":2}},"canonical_sha256":"be384d69233cdb8e7ea0d9ca221ea5f7c21242ee149489d071feb2f9dda4c0ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be384d69233cdb8e7ea0d9ca221ea5f7c21242ee149489d071feb2f9dda4c0ff","first_computed_at":"2026-07-05T11:46:21.901611Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:46:21.901611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9Sf+1GeM6imTKFrDtsLCD7fn0a61RKY6FSVkomK2yTyP57B7uUgGpg0g/nCC2iskLvZgCJfzvvgq9Ndg/kkmDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:46:21.902114Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.00830","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54ad1ce48e2c11774e869ee4604816deb5148dab1117355731ae3d2f7b452ef4","sha256:f3bb41a9e57ad5a7f0c8952bb9e0c9d7c86489f963accea8814761db1dd92847"],"state_sha256":"cf91eb51e876e3fe5b94857a6f3890f7ca63491603b79e17476d7241157eeb7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"71u8D4XNvEsd27I0YdiVTL/S/OrDp3BnQFXr+wSh31HVIQTKN7VUdHE2HA86jmj9KaWBF5BhJ0lP/UewDqZTBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T16:10:31.476806Z","bundle_sha256":"a31f630abd52befe86962885c03619cad6d36fb347ff8232df6630f2b479650f"}}