{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:EPPVEXGEILYHLRXKVNLDM3UJKZ","short_pith_number":"pith:EPPVEXGE","canonical_record":{"source":{"id":"2607.08953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:27:29Z","cross_cats_sorted":[],"title_canon_sha256":"87bd4fa8e252a6b468f28fbffc103a6a74ff7c9484df50d1ab5c5bf6b01cd98c","abstract_canon_sha256":"f669d6922459eee9950151c69d247fb0712bbffda906dcb9501f6f683189d887"},"schema_version":"1.0"},"canonical_sha256":"23df525cc442f075c6eaab56366e89564492c9d93461fbe3b2284c8abffabdbd","source":{"kind":"arxiv","id":"2607.08953","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.08953","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"arxiv_version","alias_value":"2607.08953v1","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08953","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"pith_short_12","alias_value":"EPPVEXGEILYH","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"pith_short_16","alias_value":"EPPVEXGEILYHLRXK","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"pith_short_8","alias_value":"EPPVEXGE","created_at":"2026-07-13T00:17:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:EPPVEXGEILYHLRXKVNLDM3UJKZ","target":"record","payload":{"canonical_record":{"source":{"id":"2607.08953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:27:29Z","cross_cats_sorted":[],"title_canon_sha256":"87bd4fa8e252a6b468f28fbffc103a6a74ff7c9484df50d1ab5c5bf6b01cd98c","abstract_canon_sha256":"f669d6922459eee9950151c69d247fb0712bbffda906dcb9501f6f683189d887"},"schema_version":"1.0"},"canonical_sha256":"23df525cc442f075c6eaab56366e89564492c9d93461fbe3b2284c8abffabdbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T00:17:29.498906Z","signature_b64":"qdxf9hpcj6LrRh8ZBmW3Mb4nhWAShIRS7kKc5CRQm5YogYTItksfvODF8AHBTJxmADAN/EyoV0/mwLlLpPu2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23df525cc442f075c6eaab56366e89564492c9d93461fbe3b2284c8abffabdbd","last_reissued_at":"2026-07-13T00:17:29.497032Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T00:17:29.497032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.08953","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":"2026-07-13T00:17:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t2FD3xwOmvud2oRP+h8oBbivWH8pFkcoaAGtKrl+Cl9pRleF0ar6BtmkeCZMXNArp95fZ48XZwT60nVGFciEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T17:58:09.627505Z"},"content_sha256":"48e503616761ce4473a91c803c1f0858f7cdcdc392f2d2b8b9a987d79968e4b4","schema_version":"1.0","event_id":"sha256:48e503616761ce4473a91c803c1f0858f7cdcdc392f2d2b8b9a987d79968e4b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:EPPVEXGEILYHLRXKVNLDM3UJKZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Isabella Mixton-Garcia, Nick Souligne, Vignesh Subbian","submitted_at":"2026-07-09T21:27:29Z","abstract_excerpt":"Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle. This work presents FairSelect, a toolkit for systematically evaluating fairness mitigation strategies applied individually and in combination across preprocessing, inprocessing, and postprocessing stages. FairSelect supports mult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08953","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/2607.08953/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":"2026-07-13T00:17:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i93bPTmfKNVtJ/WudHYUc9Lv5m+IDrHWc4TgFu8gCIhUuR8T97edMtWiTAdELUGuVFpOKQFv9SV4XX25a9DCDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T17:58:09.628001Z"},"content_sha256":"9921b71500867be910eadc0613aa730dc3e964895af9db74449db84ec560f8b2","schema_version":"1.0","event_id":"sha256:9921b71500867be910eadc0613aa730dc3e964895af9db74449db84ec560f8b2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ/bundle.json","state_url":"https://pith.science/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ/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":"2026-08-14T17:58:09Z","links":{"resolver":"https://pith.science/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ","bundle":"https://pith.science/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ/bundle.json","state":"https://pith.science/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EPPVEXGEILYHLRXKVNLDM3UJKZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:EPPVEXGEILYHLRXKVNLDM3UJKZ","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":"f669d6922459eee9950151c69d247fb0712bbffda906dcb9501f6f683189d887","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:27:29Z","title_canon_sha256":"87bd4fa8e252a6b468f28fbffc103a6a74ff7c9484df50d1ab5c5bf6b01cd98c"},"schema_version":"1.0","source":{"id":"2607.08953","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.08953","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"arxiv_version","alias_value":"2607.08953v1","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08953","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"pith_short_12","alias_value":"EPPVEXGEILYH","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"pith_short_16","alias_value":"EPPVEXGEILYHLRXK","created_at":"2026-07-13T00:17:29Z"},{"alias_kind":"pith_short_8","alias_value":"EPPVEXGE","created_at":"2026-07-13T00:17:29Z"}],"graph_snapshots":[{"event_id":"sha256:9921b71500867be910eadc0613aa730dc3e964895af9db74449db84ec560f8b2","target":"graph","created_at":"2026-07-13T00:17:29Z","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/2607.08953/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle. This work presents FairSelect, a toolkit for systematically evaluating fairness mitigation strategies applied individually and in combination across preprocessing, inprocessing, and postprocessing stages. FairSelect supports mult","authors_text":"Isabella Mixton-Garcia, Nick Souligne, Vignesh Subbian","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:27:29Z","title":"FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08953","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":"sha256:48e503616761ce4473a91c803c1f0858f7cdcdc392f2d2b8b9a987d79968e4b4","target":"record","created_at":"2026-07-13T00:17:29Z","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":"f669d6922459eee9950151c69d247fb0712bbffda906dcb9501f6f683189d887","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:27:29Z","title_canon_sha256":"87bd4fa8e252a6b468f28fbffc103a6a74ff7c9484df50d1ab5c5bf6b01cd98c"},"schema_version":"1.0","source":{"id":"2607.08953","kind":"arxiv","version":1}},"canonical_sha256":"23df525cc442f075c6eaab56366e89564492c9d93461fbe3b2284c8abffabdbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23df525cc442f075c6eaab56366e89564492c9d93461fbe3b2284c8abffabdbd","first_computed_at":"2026-07-13T00:17:29.497032Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-13T00:17:29.497032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qdxf9hpcj6LrRh8ZBmW3Mb4nhWAShIRS7kKc5CRQm5YogYTItksfvODF8AHBTJxmADAN/EyoV0/mwLlLpPu2BQ==","signature_status":"signed_v1","signed_at":"2026-07-13T00:17:29.498906Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.08953","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:48e503616761ce4473a91c803c1f0858f7cdcdc392f2d2b8b9a987d79968e4b4","sha256:9921b71500867be910eadc0613aa730dc3e964895af9db74449db84ec560f8b2"],"state_sha256":"28d9eff8a4f1e373fedc6b54aea7e315bd66db1626148cb85fb03afea26f77ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cDdeHDqZDsRQRroBvmLN8+GQEZMUIVWaYR8ChUhOFnY+g4Npsw1EefZcX/YRTRSVr8VkLRRPXu9zqMj/5RHaCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T17:58:09.632490Z","bundle_sha256":"aef9e43a607e330620b0066c2fe3e70a007d3a7220797dc48ba36735e01646ee"}}