{"as_of":"2026-08-04T00:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dcaee95175f8a942912a0cb979ecbf0f9b7e25321711a3f21ab9ff4fa1ab0e24","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T06:07:45.509382Z","state":"measured"},{"denominator":83,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":83,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-03T06:30:56.289259+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/2606.23515/citation-record","integrity":"/paper/2606.23515/integrity","json":"/paper/2606.23515/citation-record.json","paper":"/paper/2606.23515"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24432/c5n30t","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:09:03.476642Z","title":"UCI Machine Learning Repository, 2000","venue":null,"work_id":"9cac4b67-b577-4954-b791-61897a3525ed","year":2000},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:483a5afcffc8337280ef87827110e3287d348867a657888fc071f39cffc732b0","observation_id":"beb56b7f-cfe2-4fba-ab24-b1c7820fa344","resolution":{"observed_at":"2026-06-26T06:09:03.477718Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.06879","last_updated":"2022-06-17T13:19:33Z","snapshot_observed_at":"2026-07-06T09:28:19.550239Z","submitted_at":"2020-06-11T23:57:55Z","title":"Active Sampling for Min-Max Fairness","version":3},"cited_work":{"arxiv_id":"2006.06879","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.06879","snapshot_observed_at":"2026-07-04T12:49:51.943842Z","title":"Active sampling for min-max fairness.arXiv preprint arXiv:2006.06879, 2020","venue":null,"work_id":"cef4a49f-0020-4a5f-a648-6c8d19f2f69a","year":2006},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2006.06879","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:884f4f805251993a88e48a2a363cccc562491c09a968c77ff455032b0d099502","observation_id":"62edc5c3-bf10-43d3-a5d7-b3e21db30965","resolution":{"observed_at":"2026-07-04T12:49:51.945297Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05113","last_updated":"2020-01-24T16:52:25Z","snapshot_observed_at":"2026-08-03T15:51:59.861728Z","submitted_at":"2019-10-11T12:28:52Z","title":"Fairness in Clustering with Multiple Sensitive Attributes","version":2},"cited_work":{"arxiv_id":"1910.05113","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05113","snapshot_observed_at":"2026-07-04T12:49:51.932941Z","title":"arXiv preprint arXiv:1910.05113 , year=","venue":null,"work_id":"e84d7fd8-906b-4140-9d76-caad07e5de65","year":1910},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/1910.05113","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:170a6294509514bc52b1c6924d41287ec8cd883b43c4dbecec1a8edcace5fe75","observation_id":"09c85efb-0dfc-4ac0-833b-9ae23f635fd6","resolution":{"observed_at":"2026-07-04T12:49:51.934572Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"A reductions approach to fair classification","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:8528e47394517abf2286df0f37c3d63f22b860282d919aafe78535f09d769c71","observation_id":"5346b43b-25b0-4385-b8eb-66a7aca1639b","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2022.11698","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T12:49:51.938698Z","title":"Fair active learning.Expert Sys- tems with Applications, 199:116981, 2022","venue":null,"work_id":"b58f4975-9cba-498a-a940-b8c4be140278","year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:ac5a420ad425ad015b80c76e4ecd3260007e0bc0d4936e283d80fa70283e2116","observation_id":"c306d4b6-0348-439c-a02b-167882e33bbb","resolution":{"observed_at":"2026-07-04T12:49:51.940294Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"and Li, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:9a316658d98f7bcf5741642451dd3f8eecb84016dc963f5df0f23c41e127b31f","observation_id":"503ce008-49e6-4d36-a63a-a09617436e49","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"T., Zhang, C., Krishnamurthy, A., Langford, J., and Agarwal, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:516f3d44c3f8b40cae997f0bdebfbd69a35497e26601776edde845b01d5c8907","observation_id":"eedbe561-5bad-43e5-b85c-42d4897db28a","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.05905","last_updated":"2025-08-18T23:48:57Z","snapshot_observed_at":"2026-07-06T20:48:55.297689Z","submitted_at":"2025-03-07T19:57:39Z","title":"Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Design","version":2},"cited_work":{"arxiv_id":"2503.05905","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.05905","snapshot_observed_at":"2026-07-04T12:49:51.977354Z","title":null,"venue":null,"work_id":"0ab87e36-0146-4ef8-8a89-c572d7a22ab6","year":2025},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2503.05905","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:6933a79bf7627fea9b4b1cfcd629db2d688de612625ab69f37f0f629fc9abb40","observation_id":"25aeda84-01d6-44bc-af62-0684bf88e92d","resolution":{"observed_at":"2026-07-04T12:49:51.978674Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01943","last_updated":"2018-10-03T20:18:35Z","snapshot_observed_at":"2026-07-06T07:05:57.302799Z","submitted_at":"2018-10-03T20:18:35Z","title":"AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias","version":1},"cited_work":{"arxiv_id":"1810.01943","doi":"10.48550/arxiv.1810.01943","metadata_source":"pith","pith_arxiv_id":"1810.01943","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias","venue":"cs.AI","work_id":"9129e1a1-c8a4-44be-8a48-e10ea17294e9","year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/1810.01943","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:9974f8081e6bb9ef6dda9c7912744ab12c2a035ae9e1c14583b9284825932dd5","observation_id":"beaf34d5-cada-4f9f-9ea2-0c8477825c2f","resolution":{"observed_at":"2026-07-04T12:49:51.971094Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Prediction- oriented bayesian active learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:bee582465aba4662bb8ade88344ffc3ffc030ad2355307ab65ad6aacbb3bbb52","observation_id":"c6dceaa7-4df5-4fd9-b8b7-b75368e143dc","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17249","last_updated":"2024-04-26T08:41:55Z","snapshot_observed_at":"2026-08-03T00:06:53.643258Z","submitted_at":"2024-04-26T08:41:55Z","title":"Making Better Use of Unlabelled Data in Bayesian Active Learning","version":1},"cited_work":{"arxiv_id":"2404.17249","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.17249","snapshot_observed_at":"2026-07-04T12:49:51.962382Z","title":"Making better use of unlabelled data in bayesian active learning.ArXiv, abs/2404.17249, 2024","venue":null,"work_id":"d26ef904-54d3-4639-97fc-7e6a045edb31","year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2404.17249","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:cc38d0f37b397b6d57909ce5c62dc791386d6f3b61ea9971f620be4c00bb500f","observation_id":"4e2514f8-9b8b-4b6f-8c1f-e84b1c289603","resolution":{"observed_at":"2026-07-04T12:49:51.963774Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"V ., Chades, I., and Dezfouli, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:7f832f3d42f00511a865cb483e196cb863a5609ad10fcb51fdfed7a7a8e099c2","observation_id":"81da57d1-9806-4a96-b86b-f908e343c5c9","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.22999","last_updated":"2026-07-02T18:13:40Z","snapshot_observed_at":"2026-08-03T13:48:01.479569Z","submitted_at":"2025-12-28T16:54:43Z","title":"JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference","version":2},"cited_work":{"arxiv_id":"2512.22999","doi":"10.48550/arxiv.2512.22999","metadata_source":"pith","pith_arxiv_id":"2512.22999","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"arXiv preprint arXiv:2512.22999 , year=","venue":"stat.ML","work_id":"3f5ee316-6ed3-45c5-9c0d-20c2caa4e2be","year":2025},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2512.22999","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:a77e0ac7159b7cf02542f4387f1f6591c283fdc761d649d88818a7fcf86fb2cb","observation_id":"ae1b057a-1692-453f-be99-e8280817f58b","resolution":{"observed_at":"2026-07-07T01:16:02.755369Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-18T14:51:14.438949+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-18T14:51:14.438949+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1146.353320","doi":"10.1145/3531146.3533201","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Ferrario, M","venue":null,"work_id":"64f3bd3a-8ac6-4258-9a18-e62d9463c539","year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:6633bd685583984105ccf2b9897afec0a120c72f56f4b7092d0ddc25d59387b0","observation_id":"065608dc-1575-4ae3-8705-3e1919506049","resolution":{"observed_at":"2026-06-26T06:09:03.469289Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Adaptive sampling strategies to construct equitable training datasets","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:1e0aaf69c8bf583815d0ef75d5516ef8b9e16fea64fff5a6159d36eea929311d","observation_id":"4f66a33d-2642-4d2d-b0bd-59bf64833485","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"and Haas, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:899ec005bb76bed337cbe17b9ed59b720effce1dcb3b95c4fa18afed2f8fc364","observation_id":"90911add-cd8a-4923-9fdc-67dcd5a84506","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"10.1145/3616865","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T18:43:50.246691Z","title":"doi: 10.1145/3616865","venue":null,"work_id":"54561c88-c48d-431e-98c1-b919edf85927","year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:3fbc11c7245fd747a2112337f1f374b3cdf09fc72336c81ef8c8d2fc9e70f31e","observation_id":"7203a4ce-54e4-4b9d-89c2-35b55204654a","resolution":{"observed_at":"2026-06-26T06:09:03.458670Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-11T14:19:26.268182+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T14:19:26.268182+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"R., Myung, J","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:9dd93f750e600f0e26893f35e4b47b47d78f630df5240699bf324ffc0c548ae0","observation_id":"a46d56b3-d2bd-4685-89b7-56bacd7431db","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"and Wang, X","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:f0d7374542e18f16a697c61281132facfc79b741f21e9fcd7d1f46ac7eaa4dfa","observation_id":"c9cc727a-4232-4d33-b4be-a55067a1a473","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"URL https://proceedings.mlr.press/v162/chai22a","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:26340c7cce2a5f685175f0d46c5b54604ea5a74e3d5319504b21739ed3834d9b","observation_id":"530c12c7-888e-4786-91cd-38f6e9fff8a2","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"and Verdinelli, I","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:556eb2bc735adac9be2bed917067970d29aa8fb345a68d93226dd42035388b86","observation_id":"213ae95f-53a3-4d70-8595-3882bee9e3b0","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.00056","last_updated":"2017-02-28T21:12:37Z","snapshot_observed_at":"2026-07-06T05:31:48.360247Z","submitted_at":"2017-02-28T21:12:37Z","title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","version":1},"cited_work":{"arxiv_id":"1703.00056","doi":"10.48550/arxiv.1703.00056","metadata_source":"pith","pith_arxiv_id":"1703.00056","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","venue":"stat.AP","work_id":"ab154228-e298-44d5-847f-7c6ff01d539b","year":2017},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/1703.00056","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:2b6173744f57a79f0cce5fe706e108c6ebefbc79460e97306891fb8b76805df4","observation_id":"796375a1-279f-469d-9649-356d01dddc9c","resolution":{"observed_at":"2026-07-04T12:49:51.947604Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.07261","last_updated":"2024-03-15T16:02:26Z","snapshot_observed_at":"2026-08-03T19:35:14.597714Z","submitted_at":"2023-06-12T17:44:15Z","title":"Unprocessing Seven Years of Algorithmic Fairness","version":5},"cited_work":{"arxiv_id":"2306.07261","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.07261","snapshot_observed_at":"2026-07-04T12:49:51.979756Z","title":null,"venue":null,"work_id":"ad571f11-8035-43fc-b44c-011da179b422","year":2023},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2306.07261","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:dc8d799694baed89439353aa93530ced8e7ce529992eab21bb87a692f6b2f3da","observation_id":"b5323212-fc97-4e45-b52c-72932a27f56f","resolution":{"observed_at":"2026-07-04T12:49:51.980996Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.13755","last_updated":"2020-05-26T11:40:13Z","snapshot_observed_at":"2026-08-03T17:01:59.593534Z","submitted_at":"2020-05-26T11:40:13Z","title":"Review of Mathematical frameworks for Fairness in Machine Learning","version":1},"cited_work":{"arxiv_id":"2005.13755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.13755","snapshot_observed_at":"2026-07-04T12:49:51.935875Z","title":"Review of mathematical frameworks for fairness in machine learning, 2020","venue":null,"work_id":"8596a699-f3aa-4711-976c-336aca61d3ee","year":2020},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2005.13755","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:4a0f6be8622b84706069988d2f1a1603e41ec32dd278ce38c4e1efb32feb9448","observation_id":"a4b78f5f-9ee2-4538-8caa-7773c36c8617","resolution":{"observed_at":"2026-07-04T12:49:51.937510Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Fairness through awareness,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:f8193c9d85698a4c6b9faeddb828c7b688d86306c53683f2efef2d202bb50da1","observation_id":"a9dce14d-726c-4b1a-981a-8c6f2c19aa60","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1104.3913","last_updated":"2011-11-29T04:55:13Z","snapshot_observed_at":"2026-08-03T13:55:15.400873Z","submitted_at":"2011-04-20T01:45:07Z","title":"Fairness Through Awareness","version":2},"cited_work":{"arxiv_id":"1104.3913","doi":"10.48550/arxiv.1104.3913","metadata_source":"pith","pith_arxiv_id":"1104.3913","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Fairness Through Awareness","venue":"cs.CC","work_id":"257f6a1a-c09b-4945-a496-66ffd9b4fa0e","year":2011},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/1104.3913","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:cee036d09539dec2b28a2c218836263894a8f3477d71b402978d91956f1762cc","observation_id":"e3940401-0898-415b-920f-16979401e4e7","resolution":{"observed_at":"2026-07-04T12:49:51.931549Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"T., and Leiserson, M","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:8569abe7b66a3ed18e3dcfb631cf0a883acf630866c65d1e12d007ea10207d02","observation_id":"1cab253c-c63d-4f41-8893-e57d34520e13","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2023.122842","doi":"10.1016/j.eswa.2023.122842","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"M., Saxena, A., Pei, Y ., and Pechenizkiy, M","venue":null,"work_id":"16fe7bca-a88e-474c-b415-6b0979d450be","year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:c997c5c7e1351c8a826326f2c9b6a730eee079a777631123478a775b6302aa05","observation_id":"f00b094a-32fd-4c3e-bf2b-aacf544e2e6c","resolution":{"observed_at":"2026-06-26T06:09:03.480071Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3258.278331","doi":"10.1145/2783258.2783311","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian","venue":null,"work_id":"21faf15b-6615-4482-864d-9a4a1a3908cd","year":2015},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:a8fdb93901bb034db7a2478470f293d8c6b5973bfbed6aa09d8dc568ec9bbae1","observation_id":"f5ecf91f-3a34-4a8a-86ec-027f8b38964d","resolution":{"observed_at":"2026-06-26T06:09:03.474253Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-09T10:49:45.738094+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T10:49:45.738094+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"W., Rainforth, T., and Goodman, N","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:7d206e36267f415fcdb41427f6f9cadaec36341202efcf4ecb61f9793bd57b7c","observation_id":"1f4a0d73-f814-4b83-9ddd-162e70af8541","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"W., and Rainforth, T","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:3728357d6ecd1a5a701bd3413890de74681b3b68bf5e3d2b7f694c33568eefb6","observation_id":"46697e68-e139-45a2-94a9-0784565e9787","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"R., Malik, I., and Rainforth, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:8f6573c7e2e42a15d0bfaa8f42a3bca0545855faba8cd4e488cd19d94160847a","observation_id":"cd10f63f-d0fc-49c7-91ec-7d8efe0e7ce1","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"R., Thurston, H., Varghese, P., Hong, C., and Gronsbell, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:13afbefff1b448bec008a14c974ea69e9c5ce33dc1323ab2c9f281f4aba14edc","observation_id":"8d66ba13-0518-4438-bd72-d761fffa28a3","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.08414","last_updated":"2021-07-28T05:13:20Z","snapshot_observed_at":"2026-07-06T09:21:07.659049Z","submitted_at":"2020-05-18T01:02:31Z","title":"Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs","version":3},"cited_work":{"arxiv_id":"2005.08414","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.08414","snapshot_observed_at":"2026-07-04T12:49:51.957118Z","title":"Unbiased mlmc stochastic gradient-based optimization of bayesian experimental designs.arXiv preprint arXiv:2005.08414, 2020","venue":null,"work_id":"8672c2bf-77ab-4283-a77f-dc09357572c6","year":2005},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2005.08414","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:d02b2b86b40535c82ec2842deca671d3f79d8c17e58f935c7c801191fe7562e0","observation_id":"c91f1a67-2b87-45dd-a727-713ca880e88a","resolution":{"observed_at":"2026-07-04T12:49:51.958589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Equality of opportunity in super- vised learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:be1db61c5b2ca10af518d42479af1a540870480a4cfb0f62c9f4a54ae3f75734","observation_id":"34152966-0538-4808-8bdd-a87e7c5330ac","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Equality of opportunity in supervised learning.Advances in neural information processing systems, 29, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:bd405650df2d1828fc25438a3138c1bfd2645a720fbab16325dce43507dbcea2","observation_id":"334d278d-27eb-472a-9460-e139859ee12e","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:67e7578059039f405515dd966ea5644ee7ccd792de1736c64439237d2299ce08","observation_id":"70c020c9-306f-4740-a94f-fe591119bcd9","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"1459.2025","doi":"10.1080/01621459.2025","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Journal of the American Statistical Association , author =","venue":null,"work_id":"9c8400cd-249e-4733-a0ed-0caa7f47b0b8","year":2026},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:881047fd99cc283666f6dda80c6a57c08ecbe7cfb4ccb7e0a3376d92677663b8","observation_id":"4245d252-cc8f-46c2-8538-bb9f2aa3d239","resolution":{"observed_at":"2026-06-26T06:09:03.462753Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"R., Guan, C., and Rainforth, T","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:143fbc5803872c9204861ce83e467202c422e171cc1a7d7a4f3073601a9c6a52","observation_id":"717c25f6-b1af-43e9-9596-de514d4225c6","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1112.5745","last_updated":"2011-12-24T17:53:19Z","snapshot_observed_at":"2026-07-06T02:40:01.191970Z","submitted_at":"2011-12-24T17:53:19Z","title":"Bayesian Active Learning for Classification and Preference Learning","version":1},"cited_work":{"arxiv_id":"1112.5745","doi":"10.48550/arxiv.1112.5745","metadata_source":"pith","pith_arxiv_id":"1112.5745","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Bayesian Active Learning for Classification and Preference Learning","venue":"stat.ML","work_id":"71fb63ec-d06f-4b69-b1ff-c7292cde92e0","year":2011},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/1112.5745","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:f267a357c1ee1029ab7536f4f3faa0c83338b4dd8102ea7239be15f63d219457","observation_id":"211780cb-9d62-4008-88f0-cacc2990957b","resolution":{"observed_at":"2026-07-04T12:49:51.973623Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Pushing the limits of fairness impossibility: Who’s the fairest of them all?Advances in Neural Information Processing Systems, 35: 32749–32761, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:07fd5eae6d245f16e2a6501ecae262bfec4ccec139c67939dc0f27992f830119","observation_id":"71e2fc24-b163-49f1-ab0c-f2a168b3451e","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Optimal experimental design: Formulations and computations.Acta Numerica, 33:715–840, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:3cfc3c73eeef341143e047241b66e25fbb4f1c313a328069596625294e09c94f","observation_id":"386efe8f-d909-4186-8747-46cffdeb22b4","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Amortized bayesian experimental design for decision-making","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:8674ecece1afe363df27b1bd26cd4794e6971808a417933e70c258e1ba8218c8","observation_id":"c54eef87-f90b-4211-8f5f-d7b59c357729","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2506.07259","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T12:49:51.951566Z","title":"Aline: Joint amortization for bayesian inference and active data acquisition.arXiv preprint arXiv:2506.07259","venue":null,"work_id":"a076ee7d-471f-4dfb-8e5f-10790946a9b2","year":2025},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:4ec6ce2a4341918ccdf17cfeb1d790f6639ca32320910259a0033f497a1eac86","observation_id":"8f49ef50-bfec-48e9-b2b8-247229eed9c1","resolution":{"observed_at":"2026-07-04T12:49:51.953064Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11826","last_updated":"2025-03-13T11:23:03Z","snapshot_observed_at":"2026-07-06T19:34:01.458836Z","submitted_at":"2024-10-15T17:53:07Z","title":"Bayesian Experimental Design via Contrastive Diffusions","version":2},"cited_work":{"arxiv_id":"2410.11826","doi":"10.48550/arxiv.2410.11826","metadata_source":"pith","pith_arxiv_id":"2410.11826","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Iollo, C","venue":"stat.ML","work_id":"ab0393f6-5e16-424d-aae5-f47eeec0ed39","year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2410.11826","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:808d9f686937c29022b738ba92c9a7f7610490a9145e572a1120df882a359858","observation_id":"cd326a92-21c5-44f0-bc54-b308c4a12598","resolution":{"observed_at":"2026-07-04T12:49:51.961201Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-07-18T14:51:14.926183+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-18T14:51:14.926183+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07868","last_updated":"2024-05-29T12:15:40Z","snapshot_observed_at":"2026-07-06T17:29:05.185768Z","submitted_at":"2024-02-12T18:29:17Z","title":"Nesting Particle Filters for Experimental Design in Dynamical Systems","version":4},"cited_work":{"arxiv_id":"2402.07868","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.07868","snapshot_observed_at":"2026-07-04T12:49:51.941366Z","title":"Nesting particle filters for experimental design in dynamical systems, 2024","venue":null,"work_id":"3ff3bd59-8189-42db-a985-42be51db4e1b","year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2402.07868","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:18078ae3a1f8773c974f4da68293d73157f9eac6d73144d6873585101c3414ea","observation_id":"1e763f45-3d0e-4e26-a360-fce16b5ea433","resolution":{"observed_at":"2026-07-04T12:49:51.942851Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"R., Foster, A., Kleinegesse, S., Gutmann, M., and Rainforth, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:4f01dd4ab9e90dea3fbb343b828a0443f1225f072d8f38fc9db6e16f899e82cb","observation_id":"8a6b20fb-c906-47b9-a60d-c7d5f5015a55","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Decision theory for discrimination-aware classification","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:0f9f7328518a55a1040576dc62e4f0f711839bad8e9ed7e258c670b5f340942d","observation_id":"fc634a4e-0bb9-4fbe-b892-15b001b694ad","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"A., and Rainforth, T","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:a2b6c35889c15908ac7b7621f029ac03cf320e414bf6845225ca9ccf29c8c420","observation_id":"efe77337-0567-48c5-9f13-eff22402dc56","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"10.1214/20-ba1225","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:09:03.464964Z","title":"Kleinegesse, C","venue":null,"work_id":"74c62919-ffe4-4f0c-b5f6-6f2115f8a45a","year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:4bcd88ec05be58b87169268e50d8256a459582176f69631d47b9d9adb78422d1","observation_id":"aaf8af69-a107-4c8c-9b0e-0707cf89bd8e","resolution":{"observed_at":"2026-06-26T06:09:03.466034Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Adaptive sensitive reweighting to mitigate bias in fairness-aware classification","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:4fb883d6fa39c5fab1b481dd700c71ff0a3d5c623cfb27559b14218fa4a24688","observation_id":"7aeabac9-bb72-4ab3-b111-175ee53313f6","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"8876.318613","doi":"10.1145/3178876.3186133","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"ISBN 9781450356398","venue":null,"work_id":"0b867fdc-2b94-41c1-be2b-7fe5f4e3d6cb","year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:5cb7bfa5ee79a67544d8d91d4710c2b5ed1cd2e204e73da34ce95e5d918dd62c","observation_id":"75a054af-6796-4622-aa99-eb5747cb1766","resolution":{"observed_at":"2026-06-26T06:09:03.471952Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"Adaptive sensitive reweighting to mitigate bias in fairness-aware classification","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:d955058cec66a16ac9fa4de5b3736ca9b1dd8d601e576e8bcd71cf59e1928fb5","observation_id":"2cc5db80-6b41-4739-a307-954a24e2f885","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04272","last_updated":"2022-03-08T18:47:01Z","snapshot_observed_at":"2026-07-06T12:45:39.163802Z","submitted_at":"2022-03-08T18:47:01Z","title":"Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models","version":1},"cited_work":{"arxiv_id":"2203.04272","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.04272","snapshot_observed_at":"2026-07-04T17:30:00.751719Z","title":"Policy-based bayesian experimental design for non-differentiable implicit models.arXiv preprint arXiv:2203.04272,","venue":null,"work_id":"a205003e-32ca-4054-ac0b-3ef3bd7a79fc","year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2203.04272","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:722873ac0daa33b0f935deb77c3ebae74b4f6454943cf304c8cca639d994d036","observation_id":"5f827381-073b-4c3f-b801-4a050429046e","resolution":{"observed_at":"2026-07-04T12:49:51.955928Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":null,"venue":null,"work_id":null,"year":1956},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:d79839e87f640189178f50c4af6d7f601054aebd70217416a07819224ed399c7","observation_id":"c0ac9d64-ab57-4614-ba24-7a4954e24538","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"V .Bayesian statistics, a review, volume 2","venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:9e4f691fd14419a56231a6726dd12f09cdadfdc23a7deaec587ee3850fd93404","observation_id":"478b6cff-8987-405b-a7e4-23c3ee8747f5","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Z., Haghgoo, B., Chen, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:a63d3836e0ff95a4bd58833225be5997fc99089c2575c40b2183d6957be3b523","observation_id":"35b4f2d0-444f-4d83-b68a-3f56d176de09","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Deep learning face attributes in the wild.2015 IEEE International Conference on Computer Vision (ICCV), pp","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:a5518fa35ccd96784219bf61c215f1dc84eb833a3da36ccd3b33ff933181d89f","observation_id":"019eab83-27be-4561-bcad-b44e7c15ea32","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"On the fairness of disentangled representations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:a51cb5452912f1a118cce314ec525e1bc7fd832a02f62081a80d97879759bb8f","observation_id":"dfbc68c7-2ec4-4694-ab6e-114b422eecde","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":null,"venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:e537b1ffd560a46cc2969940808010dab55c8928f56edcf88ac809a6c34e0eed","observation_id":"eae46cee-b837-4975-a279-749ebc945dc8","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Minimax pareto fairness: A multi objective perspective","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:c364ce295fa130f9e2ebe6419191975d1cc630f292d783998f693917c98761be","observation_id":"1d55c403-b2b1-4568-8ca1-6fa70ba9f429","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Algorithmic fairness: Choices, assumptions, and definitions.Annual Review of Statistics and Its Application, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:ad451e9816a2802d700f9b407cb87b58c7e3478b7f87258ae9d236de22334484","observation_id":"02180423-11e9-4c2d-b039-1eef520271ab","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"I., Cavagnaro, D","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:621e7b731aa735016439ec8e7070f69ebc2aee05114091914bd5b2cb5962771c","observation_id":"0a18a5b9-29b0-4f0a-8fcd-58b6f3b55e9f","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Fairness without harm: An influence- guided active sampling approach.Advances in Neural Information Processing Systems, 37: 61513–61548, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:394ae672b95dd00c7c0e6a9ae57874742fd4e90c60d69fc9266606261de61069","observation_id":"ae725211-2557-4fec-a74a-e01d8c0a59aa","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:49d500a2d4b13d0ae284dceec028ca2235d761a9baadf6efe94d4a423a05578b","observation_id":"50816a6d-ca5a-47a0-b06c-b9ced8e48e36","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"On nesting monte carlo estimators","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:dbfb343ea6f5c7e3c51a8208cbde52019154a6091450822d2272907d329a9e70","observation_id":"d452aa49-69e1-4cf6-96c7-5db665b12ea5","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"R., and Bickford Smith, F","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:577eaf44671aab80ce0783772589a548ab040732ab3e97f8b47aa49a945fbcb8","observation_id":"491b5442-14d3-4697-8a86-4c7a45c60b25","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"10.24432/c5mc89","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:09:03.459221Z","title":"M., Machado, J., and Baptista, L","venue":null,"work_id":"a03c38b1-fcbf-438b-855a-8591534c1ee0","year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:280290cbd69bdd78ad0669289cf31c3509ff2a683c511a88cd1f964251068db0","observation_id":"645df986-188d-4520-b2c1-1c55725b3771","resolution":{"observed_at":"2026-06-26T06:09:03.460333Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"C., and Fei-Fei, L","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:97b57e437b6f7025b8a5f538790cf0a507db98a35619ae9abb578e8a7e66fb5d","observation_id":"78e5ea5e-712a-40f0-a400-7b1b609b85a7","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"10.1016/j.csda.2013.08.017","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:09:03.463353Z","title":"G., Drovandi, C","venue":null,"work_id":"117ffc48-b6ec-4560-b236-608fd0e9856c","year":2014},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:ae49a4e1f36b580f754457287031ae8bda68aae98998fd15740a82b4bb2fda0c","observation_id":"743eedcf-9a5f-4427-bc3d-c12a1b5b6c93","resolution":{"observed_at":"2026-06-26T06:09:03.464489Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"G., Drovandi, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:3b572b12fee86d51b2e3c2e92cc57700d800d5e4cde40cfc0ad945331b53e611","observation_id":"00292395-cf26-4f11-b9c2-739e78d7e337","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Active learning literature survey","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:4a1d71ee9290b2244e1d823817386d3db4a4f61bd3953dc2354b624cdeb67b85","observation_id":"b70d7ab8-ee52-43d6-848e-29aa5200920c","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Promoting fairness in learned models by learning to active learn under parity constraints","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:0ed660f03b4ed30d41fbe402613c908ff644c165068af26094be001a0884526e","observation_id":"a8593b3d-fa71-424f-92df-50d4994d6532","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Adaptive sampling for minimax fair classification.Advances in Neural Information Processing Systems, 34:24535–24544, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:63cd8d013a93ea8fe23c3cbd93df22ec6a0561fa017def5dc4b9118a3c18cac8","observation_id":"4ddc8c9f-36ff-48eb-bc9a-bec43bf5c660","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Metric-fair active learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:e4622ef06fd86fe18f7c48d106e9ff1bbaf8e12287a73d5b995c22338a25d547","observation_id":"a7332ef3-9296-4416-bed4-5f2c769ea6fe","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"7976.244799","doi":"10.1145/2447976.2447990","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Discrimination in online ad delivery.Commun","venue":null,"work_id":"d77c85d6-db88-4650-80ea-d2e3463363e2","year":2013},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:b4612e196dc1561c86d55c3f91c01272f3d1b131bee49e0d51c7df676e049434","observation_id":"dbf20527-6be8-4ae7-a033-bfcfe0950ec5","resolution":{"observed_at":"2026-06-26T06:09:03.456616Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12722","last_updated":"2024-01-24T04:43:05Z","snapshot_observed_at":"2026-07-06T17:19:23.664354Z","submitted_at":"2024-01-23T12:48:27Z","title":"Falcon: Fair Active Learning using Multi-armed Bandits","version":2},"cited_work":{"arxiv_id":"2401.12722","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12722","snapshot_observed_at":"2026-07-04T12:49:51.949024Z","title":"H., Zhang, H., Park, J., Rong, K., and Whang, S","venue":null,"work_id":"afa84ba7-27fb-453b-8b41-aad6d874451d","year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2401.12722","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:224f08e1fa7b531d03a3ca35521aa364009f6a37e8f70905b41b14646623f33d","observation_id":"f94bec87-153b-443b-bcc6-765e5d30207f","resolution":{"observed_at":"2026-07-04T12:49:51.950426Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:315390679cf6c1bda13e99421b9c1d3719f5e20c230aa531635838811a03a248","observation_id":"37a8db1d-43f1-4ea2-be7c-a8605465ca7c","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:2529452253c2ae6570a1d81630e3b9e204bcbadab647ebf6798469a211eeffed","observation_id":"f02cff0f-c8c6-4610-aa94-1c223d81349d","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14987","last_updated":"2022-03-28T18:00:51Z","snapshot_observed_at":"2026-07-06T12:53:50.496350Z","submitted_at":"2022-03-28T18:00:51Z","title":"Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment","version":1},"cited_work":{"arxiv_id":"2203.14987","doi":"10.48550/arxiv","metadata_source":"doi_reference","pith_arxiv_id":"2203.14987","snapshot_observed_at":"2026-07-09T21:46:34.506658Z","title":"Dickerson","venue":"cs.AI","work_id":"5c2060c6-427c-4321-be22-49ccae439d80","year":2025},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"cited_paper":"/paper/2203.14987","citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:42464ec66fbfe3298aa067bf57d7cd4aa5b25b6aa09f6f8f85654afacce7f454","observation_id":"377ccb16-836e-4634-bc4a-b1208d0c1793","resolution":{"observed_at":"2026-06-26T06:09:03.476074Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-26T06:07:45.509382Z","title":"W., Katabi, D., and Ghassemi, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:e94f33b9374a8be52dcbbf1fce2636d092103e4ac4c66e150172d56ffaa0b084","observation_id":"22baa004-cc26-4333-8ca7-c544b0e3e3d2","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"B., Valera, I., Rogriguez, M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:32fcb20e4002ebf49fd16534c25747059d04b6d314dac45de566d4b29ec4bfa0","observation_id":"97b91bed-8cdc-41ab-846e-b7267e21802d","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","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":"2026-06-26T06:07:45.509382Z","title":"Learning fair representations","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-06-26T06:07:45.509382Z"},"links":{"citing_paper":"/paper/2606.23515"},"observation_digest":"sha256:89364f0e524b9d359c037ce23563c97abb7c8df39f8d1861e66a825f585c3532","observation_id":"4cbfcb22-503a-4b36-98f5-6d7ae46ad13a","resolution":{"observed_at":"2026-06-26T06:07:45.509382Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.23515","last_updated":"2026-06-22T16:02:08Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-03T17:40:36.411922Z","submitted_at":"2026-06-22T16:02:08Z","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":3,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":52,"verified_exact":25,"verified_fuzzy":0},"total_outbound_references":83},"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":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 0 inbound Pith citation observations for arXiv:2606.23515."}