{"as_of":"2026-08-18T04:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d20c72dc5c90fec20cb3d5a19547d9f5aac1b9978655bc4c681a7c9a5847acc","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:03:52.014897Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.14724/citation-record","integrity":"/paper/2412.14724/integrity","json":"/paper/2412.14724/citation-record.json","paper":"/paper/2412.14724"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.651945Z","title":"The use of machine learning algorithms in recommender systems: A systematic review","venue":null,"work_id":"f6cd573c-27d6-4af8-a8f8-bf05e8855947","year":2018},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.827382Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:2fb1c5be69282119dbeca144c02503c18a88cf4bcb5922c26a21ee90dfbe26a8","observation_id":"8f2fe419-58a8-431f-9efe-d1c4c2bffee9","resolution":{"observed_at":"2026-08-11T12:03:52.656703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.636548Z","title":"Credit scoring and the availability, price, and risk of small business credit","venue":null,"work_id":"41c7703c-e37d-4462-95ec-09ff128623d4","year":2005},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.833029Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:179f1b771f55c306e26cb0b9f371b92c7a57fced9a3305eb2be2cc8962d04bf9","observation_id":"067e5577-da65-4bfe-bbfc-9ea9aa22f173","resolution":{"observed_at":"2026-08-11T12:03:52.641280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.620248Z","title":"Machine bias","venue":null,"work_id":"980cada8-ca4b-4ceb-b5b2-ce3c51822970","year":2022},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.837504Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:6444035151c3fe3e893446ca9d3a01dbd96b6a1e326429001c1b25cc7d281cdd","observation_id":"e97a397b-ffe2-4fb1-b201-e39f6b7bbd91","resolution":{"observed_at":"2026-08-11T12:03:52.625268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.603604Z","title":"Amazon scraps secret ai recruiting tool that showed bias against women","venue":null,"work_id":"123433f4-117e-4b0b-8d68-e31177e567d2","year":2018},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.842062Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:5f8588de2e7b1f5b83d6371d14b203274abea07b490515dfb6ca88a36a2080c0","observation_id":"76d52868-44cd-44fd-a035-0a5d01856f3d","resolution":{"observed_at":"2026-08-11T12:03:52.608964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.587976Z","title":"Sex bias in graduate admissions: Data from berkeley","venue":null,"work_id":"2bc09e91-4b11-4d23-89e6-42066f70a2ab","year":1977},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.846482Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:3f25fa8a14d00a776f3ebd21bc82450f979018b3ead6981ee829b5cf2112d303","observation_id":"871c8dc7-8125-4b65-91bc-3da19bbb1fe9","resolution":{"observed_at":"2026-08-11T12:03:52.593281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.06876","last_updated":"2018-04-18T18:51:00Z","snapshot_observed_at":"2026-08-14T19:24:31.474740Z","submitted_at":"2018-04-18T18:51:00Z","title":"Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.06876","snapshot_observed_at":"2026-08-11T12:03:51.850982Z","title":"Gender bias in coreference resolution: Evaluation and debiasing methods","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.850982Z"},"links":{"cited_paper":"/paper/1804.06876","citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:f9c8e5dfae3b9b9192dec4fbb484a3c2bb2016a1cab68cb7f028600da858a844","observation_id":"dc03dd56-b12f-499c-aedc-d72b7b6f0a59","resolution":{"observed_at":"2026-08-11T12:03:51.850982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:51.856760Z","title":"Fairness through awareness","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.856760Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:c9d10687201c6225c0f054328cdc36d2c8d5dcf8d561398f6f8b2e20ac9002b1","observation_id":"bcfb3613-a118-46cf-b8ad-dda64d4664f3","resolution":{"observed_at":"2026-08-11T12:03:51.856760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:51.861432Z","title":"Learning adversarially fair and transferable representations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.861432Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:5bcd97f8c9d90ef46531eb5a906204d9258f2e591f78ab0f7658ddea74dd1e44","observation_id":"f8c0a9e3-f16b-44ee-9edf-d65da513bea3","resolution":{"observed_at":"2026-08-11T12:03:51.861432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.552903Z","title":"Learning fair representations","venue":null,"work_id":"7429697b-3c6a-4e40-a179-573ddb43377e","year":2013},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.866403Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:7f759f28dcda2345f3c7756b495a0f2f819660f5c50a0f6025a33a3827f93b35","observation_id":"66f382d7-2c5d-468d-87e3-296b0cf9350e","resolution":{"observed_at":"2026-08-11T12:03:52.557994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.537643Z","title":"Fnnc: Achieving fairness through neural networks","venue":null,"work_id":"76eaeeeb-e47e-41ae-ba7d-1f87c57dce13","year":2020},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.870968Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:497833390fdd5aaca02de81486d2d2d7bcec5336f7ea2c8f8951825669bef052","observation_id":"eba26d8d-7fc4-4152-9047-870576130815","resolution":{"observed_at":"2026-08-11T12:03:52.542439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.522526Z","title":"Consultant-2: Pre-and post- processing of machine learning applications","venue":null,"work_id":"14092520-2577-477e-a09f-c750bddddbaf","year":1995},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.876031Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:7c6b35a787127286bf7aa9b7934862b9dd61ae5cd0503404faff3560d9a9fd9b","observation_id":"32582506-01a9-4c5c-bb90-7df4a3697022","resolution":{"observed_at":"2026-08-11T12:03:52.527556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.507491Z","title":"Achieving fairness via post-processing in web-scale recommender systems","venue":null,"work_id":"28c62457-a161-45e9-bcb7-1b0a11130da6","year":2022},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.880987Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:9c3d3a2c1f7432b6d3af2ec010992da1f36b9ee91d5972f6775e630c74bd5d83","observation_id":"cd17fe71-5325-43ab-9a85-f80d5b837e33","resolution":{"observed_at":"2026-08-11T12:03:52.512012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.05807","last_updated":"2016-11-17T16:41:21Z","snapshot_observed_at":"2026-08-15T21:21:58.666896Z","submitted_at":"2016-09-19T16:08:51Z","title":"Inherent Trade-Offs in the Fair Determination of Risk Scores","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.05807","snapshot_observed_at":"2026-08-11T12:03:51.885557Z","title":"Inherent trade-offs in the fair determination of risk scores","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.885557Z"},"links":{"cited_paper":"/paper/1609.05807","citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:6e863897ee91e9544fcf6c28ea0c781d0ac49fe0b2d27a7e435fbf4a99cfe0f1","observation_id":"4e287cf0-3953-40b7-9944-3b9daba2fc68","resolution":{"observed_at":"2026-08-11T12:03:51.885557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:51.890517Z","title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.890517Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:7b6e7f04198610cf5b54eb617bea9a8bbf6ab3489851b359edf66bd2198a71b7","observation_id":"b2306452-9470-4fb5-a78e-67fdeb6c1553","resolution":{"observed_at":"2026-08-11T12:03:51.890517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.483826Z","title":"Using auc and accuracy in evaluating learning algorithms","venue":null,"work_id":"2e631dc9-d549-4a78-9625-0d8a26d6d70f","year":2005},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.895944Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:3b062c786cb8748d86211e3b7102a008fd5d3fa0d06a70af1bd7e406c3222419","observation_id":"8300c832-ca2d-4bec-8686-605859ee4293","resolution":{"observed_at":"2026-08-11T12:03:52.488143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.467946Z","title":"Ranking and empirical minimization of u-statistics","venue":null,"work_id":"21682ac1-60eb-426f-9d18-ad9b6b1f4a4c","year":2008},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.900697Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:4e60892c7aa2af5225a4440edf82cf287350b9a4f40b81f7370e1f38fd44feb4","observation_id":"bb9757eb-5daa-44fa-8aa2-e998c062e2f0","resolution":{"observed_at":"2026-08-11T12:03:52.473283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.14000","last_updated":"2022-08-12T12:58:44Z","snapshot_observed_at":"2026-08-16T18:36:18.362904Z","submitted_at":"2021-03-25T17:38:20Z","title":"Fairness in Ranking: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.14000","snapshot_observed_at":"2026-08-11T12:03:51.905710Z","title":"Fairness in ranking: A survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.905710Z"},"links":{"cited_paper":"/paper/2103.14000","citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:4dca657b96d7363a790d2b710963f9c0675e442a8cd010f9ddbeb135b49fa279","observation_id":"0dc7a6e4-54dd-4d91-8722-1b1da0ced2b4","resolution":{"observed_at":"2026-08-11T12:03:51.905710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:51.910702Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.910702Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:790617882468033731c5a2aa86a1b4023eb3d8ca1aafc55aef0a157340eb3558","observation_id":"1ea6a9fd-0a59-4a50-a49e-f72fae979122","resolution":{"observed_at":"2026-08-11T12:03:51.910702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.442149Z","title":"Equality of opportunity in supervised learning","venue":null,"work_id":"ddddbee5-5ba4-44c5-9d5d-dd7ff335083e","year":2016},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.915449Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:428e235beaebd7fb2aaaf610091fe3aac981db002debd35c595c7acd48ed536d","observation_id":"ea5f8c5c-8cc5-426d-bfcb-a86520f597e1","resolution":{"observed_at":"2026-08-11T12:03:52.447692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.426998Z","title":"Fairness definitions explained","venue":null,"work_id":"2247e41a-1028-4959-9e43-b46ead2ea33b","year":2018},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.919954Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:26e21939b57ee6a72267c3652e863a60cc5a2d25fb8ee138d1007dc199c51459","observation_id":"be0ae2be-1ba6-4b4b-a34e-2261e8714a8c","resolution":{"observed_at":"2026-08-11T12:03:52.431967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.411919Z","title":"On the problem of underranking in group-fair ranking","venue":null,"work_id":"2639caf8-d923-4b77-a2ac-6d64c288a538","year":2021},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.924641Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:41572061b26b691add5bf8af1d1ae23263567d53120f44f10843c4a508a95ed2","observation_id":"ad9ed152-0438-4ad5-b728-91675acf2265","resolution":{"observed_at":"2026-08-11T12:03:52.416733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.00780","last_updated":"2019-03-02T22:29:42Z","snapshot_observed_at":"2026-08-14T17:08:25.592308Z","submitted_at":"2019-03-02T22:29:42Z","title":"Fairness in Recommendation Ranking through Pairwise Comparisons","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.00780","snapshot_observed_at":"2026-08-11T12:03:51.929294Z","title":"Chi, and Cristos Goodrow","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.929294Z"},"links":{"cited_paper":"/paper/1903.00780","citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:a170f8ad7e4238c72bdb477955caeac73d07495c79ad7abe6d80867e7c451ae7","observation_id":"033fc841-6c94-4aca-a3d5-a4322aae7608","resolution":{"observed_at":"2026-08-11T12:03:51.929294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.397301Z","title":"Nuanced metrics for measuring unintended bias with real data for text classification","venue":null,"work_id":"24a6ceb8-0294-42db-86cc-060ae24fd1b9","year":2019},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.934373Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:3d969c9bf495439157e0fedfa15670792bf117dc35a3e7c1fed3c681d21887ca","observation_id":"691ae008-e692-49a5-8f04-ea797ea8f600","resolution":{"observed_at":"2026-08-11T12:03:52.401925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.383511Z","title":"The Fairness of Risk Scores beyond Classification: Bipartite Ranking and the XAUC Metric","venue":null,"work_id":"bba1a8db-2c9f-4269-a32a-912e5624412b","year":2019},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.938431Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:bb704705b7e6f729643a39e07ffe6a07e4dfc4a2254685b36622a53ab6e72963","observation_id":"b2de3a8b-fe67-4577-8b5b-4fbd90a3a538","resolution":{"observed_at":"2026-08-11T12:03:52.387828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.368899Z","title":"Minimax auc fairness: Efficient algorithm with provable convergence","venue":null,"work_id":"e975b5e1-3bf3-4bff-9b49-3c08b5a45b3b","year":2023},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.942747Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:290c22ffeb788091d5d439cfd60c31f040cea1b6c64122e9535e15f5df52f84b","observation_id":"223bc5c4-aef1-4285-b8b3-0ec3f85e078a","resolution":{"observed_at":"2026-08-11T12:03:52.373883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.352521Z","title":"Learning fair scoring functions: Bipartite ranking under roc-based fairness constraints","venue":null,"work_id":"81c6975a-bd40-4746-a9e6-84f92d887e6c","year":2021},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.946723Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:c652662de6638a323add54ed80f15cc635d99bc895054b61c8e0cad39fe2f6ca","observation_id":"f036dfb4-0b09-46bd-9240-3f12a41d7000","resolution":{"observed_at":"2026-08-11T12:03:52.358001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.337594Z","title":"Towards threshold invariant fair classification","venue":null,"work_id":"9ed337c3-29f4-4fab-943b-4df89e347340","year":2020},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.950492Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:ab5794be89bcf662f5a80aa61a02defa750690b340b66e3d3ab6bac2ddf87704","observation_id":"8c56a8a0-d446-480b-bd94-70842c4cd07c","resolution":{"observed_at":"2026-08-11T12:03:52.342302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.322244Z","title":"Optimized score transformation for fair classification","venue":null,"work_id":"460bb84e-ee13-41be-b66d-07d8629d5ac8","year":2020},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.954895Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:3268f7e5657652dd3dd185f6f93c91a2e5d09dc31e827af4ea7f6c216f743ad8","observation_id":"5a6e3df5-0d5c-4f74-b279-570dec8c86f8","resolution":{"observed_at":"2026-08-11T12:03:52.327761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.306043Z","title":"Towards model-agnostic post-hoc adjustment for balancing ranking fairness and algorithm utility","venue":null,"work_id":"d62f58e9-6b16-4bd1-bb21-33a1848e72cd","year":2021},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.958926Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:c48227ffd128efc39c2a0ab5483931e066d33d3aa40aba2c3845be5f78284d3a","observation_id":"1d7cb383-8a30-49d5-8988-6bdbaf08cd8a","resolution":{"observed_at":"2026-08-11T12:03:52.311227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.288158Z","title":"Fair and optimal prediction via post-processing","venue":null,"work_id":"42018d49-fc08-48c9-a5ef-c5c77c24c816","year":2024},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.963354Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:7ea59c570a40af7c9b078f9ee37d2a80bccda1e8078ed782049238f0aa67df74","observation_id":"608071ee-bd84-4e69-a142-0ab394bb9eee","resolution":{"observed_at":"2026-08-11T12:03:52.294445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.270390Z","title":"Frappé: A group fairness framework for post-processing everything","venue":null,"work_id":"fedc82b9-493f-4806-b450-09260771a66b","year":null},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.968291Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:0df290d89fc55e199e4b477ecd56ce9bfc727f834ae3b345a1f6e4339f12ec09","observation_id":"06e3bda3-9022-45b9-9200-185a09c2e433","resolution":{"observed_at":"2026-08-11T12:03:52.276589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.07261","last_updated":"2024-03-15T16:02:26Z","snapshot_observed_at":"2026-08-16T15:24:31.716747Z","submitted_at":"2023-06-12T17:44:15Z","title":"Unprocessing Seven Years of Algorithmic Fairness","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.07261","snapshot_observed_at":"2026-08-11T12:03:51.973022Z","title":"Unprocessing seven years of algorithmic fairness","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.973022Z"},"links":{"cited_paper":"/paper/2306.07261","citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:86d37b4cc554e702e08d65d155a3b8083e7feac86d4e6fc94a0d4b03703ab1c4","observation_id":"afc41cd1-fd5e-4590-b8d1-382b5b3a8be4","resolution":{"observed_at":"2026-08-11T12:03:51.973022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.254774Z","title":"Group-aware threshold adaptation for fair classification","venue":null,"work_id":"f5eded69-0dd2-4343-bb5f-ddd120301a80","year":2022},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.977774Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:ce6efbd877f7fa1fffa3a331ccd978be5f38ad6a5f8446ae2cbea3ed53449816","observation_id":"d6c39c7e-0df2-41fc-8243-e3e3100e022f","resolution":{"observed_at":"2026-08-11T12:03:52.259932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.238706Z","title":"Fairness in risk assessment instruments: Post- processing to achieve counterfactual equalized odds","venue":null,"work_id":"53d64385-b393-4255-9abc-cdc4b99bb67b","year":2021},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.982276Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:0a918e6b4d2ee2876f70b99eb133ace3b6cfc472279069b5635f5d85f900d462","observation_id":"f93cb4e0-769b-4081-9671-ff12ca56643e","resolution":{"observed_at":"2026-08-11T12:03:52.244010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.220918Z","title":null,"venue":null,"work_id":"00c70cbe-9c6c-4ffd-8d3d-656f2df8fa61","year":2000},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.987113Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:1e0cc36e4173b7528da49810c9a019f8b82c560eb877d55e7e7643dae00e1950","observation_id":"498f433b-6c63-474b-a5be-61ff79168dd9","resolution":{"observed_at":"2026-08-11T12:03:52.226943Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.203788Z","title":"Training cost-sensitive neural networks with methods addressing the class imbalance problem","venue":null,"work_id":"2a985ec4-f055-4a59-ae91-b18047336af7","year":2006},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.991971Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:a3cb67cd8ea88d647cf131d3ac7e2e01febe185df38606925faf51505e28f8a6","observation_id":"7e21cda4-5414-448e-9796-dce26786a58d","resolution":{"observed_at":"2026-08-11T12:03:52.209090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.186260Z","title":"Auc optimization vs","venue":null,"work_id":"45d2d4fe-75c1-473c-b251-777079fd1572","year":2003},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:51.996433Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:9bc51ef27b1b402cf948169117e7e17d05349dd246044e391025e6a8a17334a4","observation_id":"ec23d0a2-2d34-4dea-a9cf-ad2b50647e63","resolution":{"observed_at":"2026-08-11T12:03:52.191703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.169916Z","title":"Fairness and Machine Learning","venue":null,"work_id":"d5597736-2dce-41b2-8e73-493837fe0541","year":2019},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:52.000946Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:dfa3290f3b2fd88699d205282e9a087eb0ec26df6591f50970f1ed3f92ad31bf","observation_id":"9c523d1c-ec0e-47d7-bb86-6731754d5c42","resolution":{"observed_at":"2026-08-11T12:03:52.175115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.153378Z","title":"Is a 2000-year-old formula still keeping some secrets? The American Mathematical Monthly , 107(5):402–415, 2000","venue":null,"work_id":"cbff120c-7635-4fb8-8a52-6d12d5a957de","year":2000},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:52.005569Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:e1cfd64ee45fe6dd8722226714b7c1ffc499b2f3a01069b241c7aef380a5f303","observation_id":"4863162a-4ab5-4348-a0e7-ac822b273c64","resolution":{"observed_at":"2026-08-11T12:03:52.159012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.010245Z","title":null,"venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:52.010245Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:5ce53df12521fa512e0f3b03440ca4a14edf955a4c47b3223af8e6a4a0c34f66","observation_id":"f0bd2a8e-7fc4-40ac-89a6-b1081c0608c7","resolution":{"observed_at":"2026-08-11T12:03:52.010245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:03:52.134966Z","title":"Beyond adult and compas: Fair multi-class prediction via information projection","venue":null,"work_id":"afc3a39e-9272-483f-a533-3a425e63d236","year":2022},"citing_paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T12:03:52.014897Z"},"links":{"citing_paper":"/paper/2412.14724"},"observation_digest":"sha256:007a6697e18657bf6d6ac92f782074ea8303f8075c05999fecce2951a749c85f","observation_id":"b62fc9ba-7979-440b-bb45-53c398f3ceb0","resolution":{"observed_at":"2026-08-11T12:03:52.141669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.14724","last_updated":"2024-12-19T10:47:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T03:51:33.826883Z","submitted_at":"2024-12-19T10:47:31Z","title":"FROC: Building Fair ROC from a Trained Classifier"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":41},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.14724."}