{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RAXLDHMTYOYVLTJCD623FT7EGT","short_pith_number":"pith:RAXLDHMT","schema_version":"1.0","canonical_sha256":"882eb19d93c3b155cd221fb5b2cfe434d86cd0ad43621d3eda35a7c247b2d8a2","source":{"kind":"arxiv","id":"2501.15634","version":1},"attestation_state":"computed","paper":{"title":"Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Daniel B. Neill, Emily Black, Gordon Dai, Pavan Ravishankar, Rachel Yuan","submitted_at":"2025-01-26T18:39:54Z","abstract_excerpt":"When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of ''good'' models good enough? Recent work has highlighted that the phenomenon of model multiplicity-where multiple models with nearly identical predictive accuracy exist for the same task-has both positive and negative implications for fairness, from strengthening the enforcement of civil rights law in AI systems to showcasing arbitrariness in AI decision-making. Despi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.15634","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2025-01-26T18:39:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"de7f54da5de58752d4e4f603a31097a4fc9b1ed416856b3ac7218eebc0830d13","abstract_canon_sha256":"086b2d802e31e6bc4f4e726b96198cd5a1b91f17999334a47ce5feb36cc28a1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:32.203628Z","signature_b64":"HZ+mOfaKcuwEu4C0Fx/eUFAZ8mATUusD9kEEZ0uY8+pH2KO02zykJ4IXrRN8S56IE0efNTChMQkg5kArwBcPDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"882eb19d93c3b155cd221fb5b2cfe434d86cd0ad43621d3eda35a7c247b2d8a2","last_reissued_at":"2026-07-05T10:05:32.203022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:32.203022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Daniel B. Neill, Emily Black, Gordon Dai, Pavan Ravishankar, Rachel Yuan","submitted_at":"2025-01-26T18:39:54Z","abstract_excerpt":"When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of ''good'' models good enough? Recent work has highlighted that the phenomenon of model multiplicity-where multiple models with nearly identical predictive accuracy exist for the same task-has both positive and negative implications for fairness, from strengthening the enforcement of civil rights law in AI systems to showcasing arbitrariness in AI decision-making. Despi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15634","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/2501.15634/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.15634","created_at":"2026-07-05T10:05:32.203092+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.15634v1","created_at":"2026-07-05T10:05:32.203092+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15634","created_at":"2026-07-05T10:05:32.203092+00:00"},{"alias_kind":"pith_short_12","alias_value":"RAXLDHMTYOYV","created_at":"2026-07-05T10:05:32.203092+00:00"},{"alias_kind":"pith_short_16","alias_value":"RAXLDHMTYOYVLTJC","created_at":"2026-07-05T10:05:32.203092+00:00"},{"alias_kind":"pith_short_8","alias_value":"RAXLDHMT","created_at":"2026-07-05T10:05:32.203092+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05286","citing_title":"Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT","json":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT.json","graph_json":"https://pith.science/api/pith-number/RAXLDHMTYOYVLTJCD623FT7EGT/graph.json","events_json":"https://pith.science/api/pith-number/RAXLDHMTYOYVLTJCD623FT7EGT/events.json","paper":"https://pith.science/paper/RAXLDHMT"},"agent_actions":{"view_html":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT","download_json":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT.json","view_paper":"https://pith.science/paper/RAXLDHMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.15634&json=true","fetch_graph":"https://pith.science/api/pith-number/RAXLDHMTYOYVLTJCD623FT7EGT/graph.json","fetch_events":"https://pith.science/api/pith-number/RAXLDHMTYOYVLTJCD623FT7EGT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT/action/storage_attestation","attest_author":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT/action/author_attestation","sign_citation":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT/action/citation_signature","submit_replication":"https://pith.science/pith/RAXLDHMTYOYVLTJCD623FT7EGT/action/replication_record"}},"created_at":"2026-07-05T10:05:32.203092+00:00","updated_at":"2026-07-05T10:05:32.203092+00:00"}