{"as_of":"2026-08-16T08:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b4bf4632d20582129be3c86bb568fb13579920988d4391da09fde6f87c949863","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:56:33.581348Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2507.08866/citation-record","integrity":"/paper/2507.08866/integrity","json":"/paper/2507.08866/citation-record.json","paper":"/paper/2507.08866"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.162188Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.162188Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:096a98090be21fcf4857305d7f353f9e8bb18a33545a9cec4f9dff03bdb48799","observation_id":"e72a3053-4bda-4461-9eca-559667982cf8","resolution":{"observed_at":"2026-08-06T18:56:33.162188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.168085Z","title":"Machine bias","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.168085Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:a1c010971ae95d4f7b607163b03ae2777688a58e2f202706b953ca3df4850729","observation_id":"c0b749ea-a438-401b-94e8-4eb0bdffd3d2","resolution":{"observed_at":"2026-08-06T18:56:33.168085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.175204Z","title":"Dissecting racial bias in an algorithm used to manage the health of populations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.175204Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f6ee2683640905928a0ba58b32d84db0ed28df35f75ccd3e85426b1e4081d046","observation_id":"1afb6d43-e998-40ee-800f-ac226fc431d1","resolution":{"observed_at":"2026-08-06T18:56:33.175204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.552416Z","title":"Towards a standard for identifying and managing bias in artificial intelligence","venue":null,"work_id":"a0d72371-2ed5-4ed9-82c9-35ebe6141b97","year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.186421Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:af1cfbe5487cbd6093fcc22c6994a0cce24879fa7a7269b518bd57601d25f17a","observation_id":"85d5670c-7728-4248-9658-26ea4bf9e67d","resolution":{"observed_at":"2026-08-06T18:56:36.557991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.533840Z","title":"Artificial intelligence act","venue":null,"work_id":"a6e787da-c431-4881-857b-cce06a6049c5","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.192251Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:ee5b3cfc2a34af10f33087273674d237d5f5fcaec7726bdaf9d2a41891fca0ca","observation_id":"956cde05-af99-4c38-bd69-a373a30a5e92","resolution":{"observed_at":"2026-08-06T18:56:36.539963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.515768Z","title":"Information technology — artificial intelligence (ai) — bias in ai systems and ai aided decision making, 2021","venue":null,"work_id":"2d2aa799-ed7d-4b5f-bd62-23a91c38a663","year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.202079Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:51603a62944d40d63f6a8f068294dd67807c94dfc25bd03def8cb0483ccad42d","observation_id":"6b196046-8d9c-4052-aea9-7adb288e4bec","resolution":{"observed_at":"2026-08-06T18:56:36.522318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.209406Z","title":"Gillis, Vitaly Meursault, and Berk Ustun","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.209406Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:3fb6a19812f3b100d9a851afd32761ab84febd9c7bcd6395705c85a45821f407","observation_id":"7bcaca34-dcd0-4034-a699-a615184dc0e6","resolution":{"observed_at":"2026-08-06T18:56:33.209406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.215428Z","title":"Fairness and bias in algorithmic hiring","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.215428Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:8fe9548d50c9d22c709222cdfefc889d757cec2c0c4e1b2b8e57403c4e1f2250","observation_id":"0aa2ece0-2128-40ba-98ad-0c3381c61029","resolution":{"observed_at":"2026-08-06T18:56:33.215428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.221555Z","title":"Baker and Aaron Hawn","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.221555Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:70da079d72d455bfa620ef614b5245b8e826c97c1c50f031bf7d10c0daaed23b","observation_id":"c8adf50c-ce05-4f37-b2cf-86a9d04e87de","resolution":{"observed_at":"2026-08-06T18:56:33.221555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.228609Z","title":"A data quality approach to the identification of discrimination risk in automated decision making systems","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.228609Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:834b0bb74b45e7ca55b719f668759e4c8d91126f8ae0791bf75b68303c18bacd","observation_id":"99541152-c393-4e60-94b9-6b1fd865423b","resolution":{"observed_at":"2026-08-06T18:56:33.228609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.495284Z","title":"Properties of fairness measures in the context of varying class imbalance and protected group ratios","venue":null,"work_id":"1093fbb1-267d-4507-984c-ad4b01a053f6","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.233839Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:6aa612f7fe9af45869c83e9b270724fbb2c30fa471ac306f6b34cdadc28c3878","observation_id":"e4c046b7-f6a0-464d-b1f5-a535b89f2214","resolution":{"observed_at":"2026-08-06T18:56:36.502529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20089","last_updated":"2024-06-26T07:35:30Z","snapshot_observed_at":"2026-08-15T18:23:04.487427Z","submitted_at":"2024-03-29T09:54:09Z","title":"Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.20089","snapshot_observed_at":"2026-08-06T18:56:33.238797Z","title":"u ller, Conradin Braun, Domenique Zipperling, and Niklas K \\","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.238797Z"},"links":{"cited_paper":"/paper/2403.20089","citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f3b8ad33718d84a007f821d20f164f9148cedc67ec6182d111a2d0cc22b17531","observation_id":"29b3d04f-7604-412f-a456-fa2bc4705871","resolution":{"observed_at":"2026-08-06T18:56:33.238797Z","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":"2022.10322","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.606200Z","title":"Auditing fairness under unawareness through counterfactual reasoning","venue":null,"work_id":"35c9c44e-61ad-40db-bb61-a5624a109978","year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.243971Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:950fbf089a6750dd88b7f79363c3ddad0c1432b153e180796e50e2d9b8c06706","observation_id":"194c4f71-ef3a-4977-b443-b7732e8d62e5","resolution":{"observed_at":"2026-08-06T18:56:35.616490Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.12518","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.508132Z","title":"Measuring fairness in credit ratings","venue":null,"work_id":"b4c98dce-4178-48a5-a322-9872a5f83fdd","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.248826Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:378b2cd0f371336d24b5d4857de4f62f64c84e88e8bdd334b8fa0899d9b67810","observation_id":"a490e68f-1da6-4246-98b6-dca238c07274","resolution":{"observed_at":"2026-08-06T18:56:35.516728Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.253516Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.253516Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:97387c4cabdcdf99a4986e9b1af4fa74350c66e9cd8d45a41ecc8959abe90fa6","observation_id":"9518b093-2121-48c6-9b26-57216cb6fdf1","resolution":{"observed_at":"2026-08-06T18:56:33.253516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.258589Z","title":"Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.258589Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:d31e2fe51eba58bfdd9926d0b4276d07180691847a7566937fdf880d0950a49c","observation_id":"c3dbeaff-9469-4bc4-9f33-745afc860bcb","resolution":{"observed_at":"2026-08-06T18:56:33.258589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.461115Z","title":"Long-term fairness with unknown dynamics","venue":null,"work_id":"765aba79-e2e4-4962-a6a4-fe6987484b80","year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.263683Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:4813caf61751874fc12ee8d89a1df662e9420a87569b23befc5b5dfefcc321a1","observation_id":"dd0a8bc4-82f1-46c0-bf6c-254f95f81f8d","resolution":{"observed_at":"2026-08-06T18:56:36.466556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.443445Z","title":"Cruz and Moritz Hardt","venue":null,"work_id":"c503c9ed-9913-4e51-958c-469744884f32","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.268897Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:22671c49a8de40140fa2939d47c54b2d9216a31af41a563783366f8b3a44fe13","observation_id":"bf6c9e6f-679f-448b-824a-b340b3edbc8b","resolution":{"observed_at":"2026-08-06T18:56:36.448339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.273873Z","title":"Learning fair representations via rebalancing graph structure","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.273873Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:cd48761f739eafdb43bb93df2290c36ec7c6add0fa23d2f26472ea5848a8e273","observation_id":"c387875d-2103-42a4-9711-343afa3ea7d6","resolution":{"observed_at":"2026-08-06T18:56:33.273873Z","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":"2023.12284","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.220695Z","title":"FAL-CUR: fair active learning using uncertainty and representativeness on fair clustering","venue":null,"work_id":"174047f5-16e9-45a9-b409-2db5213bec2b","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.280439Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:585db639cdb7663fb0c10b282df2e3ce1579c10600c1d513ec8e489a514f2771","observation_id":"21bf0eed-bec0-4177-b015-926d866c726c","resolution":{"observed_at":"2026-08-06T18:56:35.229950Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.425918Z","title":"Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment","venue":null,"work_id":"33204992-b1fb-4092-9a2c-42e88203f58f","year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.285530Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:d722b7efe0e74234914ea300843ddabd38911b6dbdc50c750b9b93de38a4060f","observation_id":"a752d47d-f874-43bc-8f27-8dbdfee8b239","resolution":{"observed_at":"2026-08-06T18:56:36.431178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08519","last_updated":"2024-04-17T08:36:50Z","snapshot_observed_at":"2026-08-13T00:31:53.736633Z","submitted_at":"2024-04-12T14:59:58Z","title":"Non-discrimination law in Europe: a primer for non-lawyers","version":2},"cited_work":{"arxiv_id":"2404.08519","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.08519","snapshot_observed_at":"2026-08-06T18:56:35.129839Z","title":"Non-discrimination law in Europe: a primer for non-lawyers","venue":"cs.CY","work_id":"ffae2edb-808e-478a-8765-0fbd1e6560cd","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.290742Z"},"links":{"cited_paper":"/paper/2404.08519","citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:a0eb0db203c96231b8e93e463e2297e596200e647401f3bbf27c6d4f5fb9e0d8","observation_id":"ba840c95-f215-4735-a673-c4f20bd0fe3a","resolution":{"observed_at":"2026-08-06T18:56:35.135438Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.303310Z","title":"Bias on demand: A modelling framework that generates synthetic data with bias","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.303310Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:91a5b9ca8f27a5ebe8fadaebd5d8c057496e63967b132b9838b9fb2a9f573892","observation_id":"efdedcc7-e940-4140-803b-c841a6e4ccd4","resolution":{"observed_at":"2026-08-06T18:56:33.303310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.309693Z","title":"On explaining unfairness: An overview","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.309693Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:1c37e48fb933e42e1e3b8c6a73845b7b75944ed5a47cb110348aae8eff9703b6","observation_id":"a9f2991b-d4f4-45b4-912e-811c5d51364d","resolution":{"observed_at":"2026-08-06T18:56:33.309693Z","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":"10.1145/3530787","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Detecting risk of biased output with balance measures","venue":"Journal of Data and Information Quality","work_id":"31976a0e-3f2b-4246-9a3e-802d406720e2","year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.316857Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:bd6174e5a6bc34abeaf5f54d8b66a800fb7ededb168fed3602a64bb7c8e2927f","observation_id":"56db4888-b469-49a8-8116-f23bac6f42d1","resolution":{"observed_at":"2026-08-06T18:56:33.714861Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.322203Z","title":"Measuring imbalance on intersectional protected attributes and on target variable to forecast unfair classifications","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.322203Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:e6177786e6e7b4c4aad52dff8fe58449741a078ae630984d86d98bed81730748","observation_id":"d1969b9c-6e9f-443b-adf7-7d8f5601e82d","resolution":{"observed_at":"2026-08-06T18:56:33.322203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.327341Z","title":"Wallach, Hal Daum \\' e III, and Kate Crawford","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.327341Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:5e8a9dc2132d4bc1376aed6141cf5e88a24c1d88d50ecb25ce68e9b7a46ddf37","observation_id":"9904165b-34ef-4ced-a9bd-a8861f9d1bbb","resolution":{"observed_at":"2026-08-06T18:56:33.327341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.405811Z","title":"The dataset nutrition label","venue":null,"work_id":"1b9d7cf1-8ca2-4e1d-9d7e-1d94ff033e22","year":2020},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.332194Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:58359d53889e42e65412fa7487c0aa8e230d38eca8bc258ba9dffb8a3c6d13f0","observation_id":"b0a6ae34-5731-4ab0-9c7e-dcf83852707e","resolution":{"observed_at":"2026-08-06T18:56:36.411700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.336867Z","title":"Data cards: Purposeful and transparent dataset documentation for responsible AI","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.336867Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:3d6e2324291546512b25e08610cf4c10ecc7e4f56fe4f66f9ba7a03d517dfb24","observation_id":"44260314-8f1e-4dd7-934a-a631ef058291","resolution":{"observed_at":"2026-08-06T18:56:33.336867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.341656Z","title":"Algorithmic fairness datasets: the story so far","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.341656Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:78d5c0d04588148380254ea3c14d356bff74b5fd6a70792859af188409409856","observation_id":"55c8bd78-d564-408b-992e-a360686502e7","resolution":{"observed_at":"2026-08-06T18:56:33.341656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.386337Z","title":"Ai documentation: A path to accountability","venue":null,"work_id":"5230061b-bcb4-4e16-960f-9016d14a9be9","year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.346778Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:0d2ec43d5ac20c607813490b8040dd126f74b4746edd72779628616cc9b1d425","observation_id":"427ae7f1-93f0-4875-a73a-4da5b06a6e7b","resolution":{"observed_at":"2026-08-06T18:56:36.391822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-49008-8","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":"Completeness of datasets documentation on ML/AI repositories: An empirical investigation","venue":"Lecture notes in computer science","work_id":"a5723100-578d-4b5a-a6af-5a566341feb9","year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.351983Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:0285fad74331f4d0e1647e9e59a1653e837e0a870b224a1438e74fca8a334cdb","observation_id":"41ed07a9-98ba-4c27-a94d-6b0baa361a21","resolution":{"observed_at":"2026-08-06T18:56:36.374231Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-68024-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":"Pandit, Sven Schade, Declan O'Sullivan, and Dave Lewis","venue":"Lecture notes in computer science","work_id":"b93e4157-89ad-4ff1-9724-0aea121b2131","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.357256Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:7899acaf0d34ead3ee60ef550d693f0de20af125f6eccb70d42bbee2e799c321","observation_id":"d43cf557-b2a9-4dfc-bbaa-1229b68a9f91","resolution":{"observed_at":"2026-08-06T18:56:36.356580Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.362305Z","title":"everyone wants to do the model work, not the data work","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.362305Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:b4b3d8250021a7153046585596f425ec98f00956bf18f9fed463263334d0cee0","observation_id":"ea4cbcab-f0e9-4c38-912b-6ab46b7d90ca","resolution":{"observed_at":"2026-08-06T18:56:33.362305Z","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":"2024.33619","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:34.665703Z","title":"Metrics for dataset demographic bias: A case study on facial expression recognition","venue":null,"work_id":"322c47c9-f964-4e39-87a1-194ea68c35c0","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.367213Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:48f4dc8baff9e071b5443c463ff26cc4e9ac3cb965e54878eccca459b82c998b","observation_id":"2f6ab1dd-568e-4016-92c9-cad3aff0bbcb","resolution":{"observed_at":"2026-08-06T18:56:34.675415Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.372588Z","title":"A survey on bias and fairness in machine learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.372588Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:2266e7af1c5f191e2f58c6fc4ee88662fa6ef56e10e1611c5edbdc6e70dea51d","observation_id":"44fc2d28-a5b9-4bdc-a34c-9b2869e7b0b9","resolution":{"observed_at":"2026-08-06T18:56:33.372588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.377331Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.377331Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:61cc4ccaf474165f38147ba82025f68c94ea02a2861592683d02ed1fdc478778","observation_id":"555782d1-d10a-486e-8665-730fd05d2b0d","resolution":{"observed_at":"2026-08-06T18:56:33.377331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.331713Z","title":"Invisible women: Data bias in a world designed for men","venue":null,"work_id":"bba6fd66-ec18-479c-b312-0f38d7063c30","year":2019},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.381986Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:1e74a5697b8bea744378da3970bdf855fabdb11f6f7a5f4ed956d89fa0460f9d","observation_id":"ce5a5769-d3d0-41c3-b7a9-21b6e4e6a166","resolution":{"observed_at":"2026-08-06T18:56:36.338384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.311669Z","title":"The uncounted","venue":null,"work_id":"3bf1b571-7e11-4460-9021-81903ccd1034","year":2020},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.386416Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:64dd5442345845a99e87022354cf93863ad2abf4d6178373e1389293fc8dad21","observation_id":"49864623-99e6-4c72-a755-3108d6e27faa","resolution":{"observed_at":"2026-08-06T18:56:36.318271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.293142Z","title":null,"venue":null,"work_id":"48c68ae6-855d-4a39-9b88-ed57f6486677","year":2017},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.391108Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:bd0bacb58fd42887a9e4036c3437e2ea4fab2c067ddcc69e993c5ef3f58ab42d","observation_id":"a8c4fe16-2f7b-4771-be44-9a8348b94958","resolution":{"observed_at":"2026-08-06T18:56:36.298736Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.270311Z","title":"Gender shades: Intersectional accuracy disparities in commercial gender classification","venue":null,"work_id":"b442497a-4815-4e80-9473-334d67deacfd","year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.395757Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:4c73cb65534b1e2de2e4391d9344dc5e7bef0a2ab4c3db8d2736939479dbb536","observation_id":"c07a1ebe-73c3-4649-90fc-0f0f0415a492","resolution":{"observed_at":"2026-08-06T18:56:36.277876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.247529Z","title":"Fairness and Machine Learning: Limitations and Opportunities","venue":null,"work_id":"fdd42739-99aa-43bb-b228-7f17aa2368ff","year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.400358Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:86f3cd02412ece6ebef846b44994befc79efa34e597d6354aa054c9b5b543f92","observation_id":"9eab6bab-e7fd-49bf-88d3-41ccf11ad5dc","resolution":{"observed_at":"2026-08-06T18:56:36.253961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.404877Z","title":"The impact of group membership bias on the quality and fairness of exposure in ranking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.404877Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:99b74c7c2cf79ea66a07a3f89f1a0c34979649e8567d75440950e3bdec18bf4f","observation_id":"8d0c5cfc-6b4c-49c8-a10f-ac82964793a1","resolution":{"observed_at":"2026-08-06T18:56:33.404877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.226439Z","title":"It's compaslicated: The messy relationship between RAI datasets and algorithmic fairness benchmarks","venue":null,"work_id":"2c085718-c961-4d67-b058-eedf768528d4","year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.409838Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:e3f92febc77a94a79466d97126f0e90163347783ddbd96fb8f059a60d6d1d2c0","observation_id":"c2f133b5-5a1a-4823-b68e-8a676dc90431","resolution":{"observed_at":"2026-08-06T18:56:36.232785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.205663Z","title":"Potential biases in machine learning algorithms using electronic health record data","venue":null,"work_id":"e7a1b806-8cfc-4b31-8ffb-fa90975b7791","year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.414616Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:0c42f231c0d72c05266b968da0e8b88af14080a582fc7adcb3af7fdf0ad84ade","observation_id":"d0de057c-539d-4333-b686-aa05b182e8e0","resolution":{"observed_at":"2026-08-06T18:56:36.212075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.182755Z","title":"Unintended bias and identity terms, 2018","venue":null,"work_id":"55a13ec6-1ed4-44f4-96f6-3833390d69d8","year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.419313Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:b19f6ccbeb97d12756abc58a3863ec86f4a22e6af571b786f41095074e16d2c2","observation_id":"5ff26288-8a48-4f3c-8ea1-5bcbba4a9261","resolution":{"observed_at":"2026-08-06T18:56:36.192517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.424038Z","title":"Data preprocessing techniques for classification without discrimination","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.424038Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:cd83fb74a00c6c7fecb8558ca9ce527d0f6571bc9e7c515bf25899135227d9e7","observation_id":"a2b8762d-ae30-4609-893d-b155eb4df9c5","resolution":{"observed_at":"2026-08-06T18:56:33.424038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.429378Z","title":"Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.429378Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:84a31a9961f18ea3e114e659ea3b6615f7b970e28c377b6b5eef6f17868e356f","observation_id":"337d8d86-9c19-451b-9a5e-ac2975d67fb7","resolution":{"observed_at":"2026-08-06T18:56:33.429378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.163301Z","title":"Aim: Attributing, interpreting, mitigating data unfairness","venue":null,"work_id":"2968d531-9457-4d0c-8f26-5294197ad6e5","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.439793Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:6a63f2df2e63fb5e37ca605a17b203b4cc4170980ebca479f347ab4ac65b0284","observation_id":"f47241f6-b032-4f70-a910-8a853c1bb9d7","resolution":{"observed_at":"2026-08-06T18:56:36.169259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.444334Z","title":"Jacobs and Hanna Wallach","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.444334Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:fabf4d454179664df62270b93d7db1a5b7c2c11fc405314180b9525668106cc6","observation_id":"fc144b5e-3b3b-48da-80c5-6e9efbb97aa0","resolution":{"observed_at":"2026-08-06T18:56:33.444334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.449131Z","title":"Comparison and benchmark of name-to-gender inference services","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.449131Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:74e1003d2a395bdc61a5ef75a64b8fcdf6557ee9de86d4944342d1dc6cc4534a","observation_id":"b3ce40c2-a470-4222-83bf-32cb9c1c95e0","resolution":{"observed_at":"2026-08-06T18:56:33.449131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.454282Z","title":"Demographic prediction based on user's browsing behavior","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.454282Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:74110aae686068a78225c5d3cb05ea20d9f12e081851eb9d8f12a5938f8e799d","observation_id":"1aa31adf-4fe1-4fe0-8c03-160d9d0c3301","resolution":{"observed_at":"2026-08-06T18:56:33.454282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.145758Z","title":"Equality of opportunity in supervised learning","venue":null,"work_id":"a71f7ec0-14af-48da-a538-f62f2f077169","year":2016},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.459567Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:5162a73c77d08e4029b013245ce3e595f9e35ee50c4caea4597b294b6614e675","observation_id":"cf3d9bb8-4cbc-4f7c-b97d-d47f88931c29","resolution":{"observed_at":"2026-08-06T18:56:36.151569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.464337Z","title":"Discrimination-aware data mining","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.464337Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:860e049dc926032257b373fcfe197833d64de4c23845829b1a21589a19002b7b","observation_id":"d0966b76-9764-4bda-8c2f-3b8a99ba990a","resolution":{"observed_at":"2026-08-06T18:56:33.464337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.469229Z","title":"Big data's disparate impact","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.469229Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:8d3ee695dd3c592f1fcad75bea999ee698c3b163ff477794ce8a1d95de1b21bf","observation_id":"3b91d61b-8551-4b7b-a768-b92a38053ef6","resolution":{"observed_at":"2026-08-06T18:56:33.469229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.112430Z","title":null,"venue":null,"work_id":"75509c7b-244a-46d1-b650-8c005c097121","year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.474262Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f65c1f7ba27a0a17c5a089e7600fe0c6e83461121ef1f8e8cf0eeacfb63c6189","observation_id":"42a17f74-cdc1-4c19-98e4-0ddc0d4d323f","resolution":{"observed_at":"2026-08-06T18:56:36.118287Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05897","last_updated":"2016-03-04T11:01:34Z","snapshot_observed_at":"2026-08-14T22:22:21.815727Z","submitted_at":"2015-11-18T18:06:24Z","title":"Censoring Representations with an Adversary","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05897","snapshot_observed_at":"2026-08-06T18:56:33.479764Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.479764Z"},"links":{"cited_paper":"/paper/1511.05897","citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:71114bbd95769da0b055f3adae88285a7b66965b21c0b97072735f38c73ddadf","observation_id":"b2a883b0-b61c-4383-9407-2f3f2c30b7f4","resolution":{"observed_at":"2026-08-06T18:56:33.479764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.484957Z","title":"Reducing unintended bias of ML models on tabular and textual data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.484957Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:8ea8e40d121b3246be47e37c98d50afcf1541b694f917b8d5bcfac4684272732","observation_id":"8995bb02-8a8a-481c-9d39-1dc0b4f1354a","resolution":{"observed_at":"2026-08-06T18:56:33.484957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.093899Z","title":"Explainability statement, 2022","venue":null,"work_id":"4f1f0ef7-1a1b-4e55-9b0a-ff0d489b68f8","year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.489488Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:ccd6c3d6908f11ae45002d5fd33ef9617e452bdc12f04f636544e972c273d665","observation_id":"76f6fd39-57cb-47bf-87aa-976b7c79f2b1","resolution":{"observed_at":"2026-08-06T18:56:36.099939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.074664Z","title":"Unbiased interviews","venue":null,"work_id":"948faf88-56d7-47d6-ad08-aa7741d0d087","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.494104Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:aabb84da0bf0d09770bb378a81d30757a9e812ec65364f3039d719db17ecb99a","observation_id":"8ba76ff5-1644-41e0-aa51-4d2c6294578f","resolution":{"observed_at":"2026-08-06T18:56:36.080537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.050709Z","title":"Navigating demographic measurement for fairness and equity","venue":null,"work_id":"b810824a-1679-4c6f-9e5f-b983eba34496","year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.498746Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:186234e9ede67e51ed7efcec5171f6f27a02b46575d626cff6add7592fc5678c","observation_id":"a0ca6e6c-8f0a-401b-9b4f-8b37c4e30d43","resolution":{"observed_at":"2026-08-06T18:56:36.056979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.032071Z","title":"Chen, and Marzyeh Ghassemi","venue":null,"work_id":"de4908eb-4000-4edd-9493-b464fbbedda9","year":2020},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.503385Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:a6ff97527e98ee7a880f1a0f918746b8c6f77d1a4b67fd6d37cd989ccab42652","observation_id":"e0592bc5-3e2d-443b-b33b-27e521e572ef","resolution":{"observed_at":"2026-08-06T18:56:36.037449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:36.013527Z","title":"Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset","venue":null,"work_id":"e82ad1a3-3cbe-4bbd-8d35-4311c76b0819","year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.507785Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:030767480f2866af9a417666c78a1bd69d5bb3fa081f89575230f738ba0774a2","observation_id":"8c059e37-3376-461b-888c-036b53ff8f98","resolution":{"observed_at":"2026-08-06T18:56:36.020840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10618-017-0506-1","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:03:40.895421Z","title":"Measuring discrimination in algorithmic decision making","venue":"Data Mining and Knowledge Discovery","work_id":"8f287347-daea-4cfb-9b3f-f466e014d27d","year":2017},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.512896Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:5b1c37fe96ec4dda961bdd47198495648cef7280b55873bffcc01e712e5c64b4","observation_id":"24e7eaad-a5b0-45b8-9012-b416752fee46","resolution":{"observed_at":"2026-08-06T18:56:33.641094Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.519397Z","title":"Fairness in deep learning: A computational perspective","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.519397Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:9a33793fdf7b386e9596380950860327fd4166db1047baf7257ac9173a12b02f","observation_id":"2bc126db-e673-4231-be5a-4135f52387a9","resolution":{"observed_at":"2026-08-06T18:56:33.519397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.994767Z","title":"On formalizing fairness in prediction with machine learning, 2018","venue":null,"work_id":"6d3b34c7-44fb-4309-8be7-fef7ff6fd519","year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.525826Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:7a88a65ce7dddf07e1ea5e738dc9da3b2196252380d3209e7908672d58a90944","observation_id":"778f13f1-380c-40f6-abec-ad4edc551214","resolution":{"observed_at":"2026-08-06T18:56:36.001471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.05826","last_updated":"2019-06-01T20:06:32Z","snapshot_observed_at":"2026-08-14T17:15:52.138315Z","submitted_at":"2019-02-15T14:48:25Z","title":"The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric","version":2},"cited_work":{"arxiv_id":"1902.05826","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.05826","snapshot_observed_at":"2026-08-06T18:56:33.770019Z","title":"The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric","venue":"cs.LG","work_id":"3e51971d-9832-4385-8828-537371ed4f05","year":2019},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.531464Z"},"links":{"cited_paper":"/paper/1902.05826","citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f6d7c6a697f3c2bd0452c8be6cb03f49575dcae1b89a848bcb1580b7ad2dc2c5","observation_id":"b19541a6-db33-4170-95ec-650a8398e7f6","resolution":{"observed_at":"2026-08-06T18:56:33.776512Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:33.536570Z","title":"Zhang, Mark Harman, and Federica Sarro","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.536570Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:2634af193e1988c4b1a6dede6f2bf8d566396f1f9d19e056b21434cafd7cd521","observation_id":"7000e5eb-d0d2-4ce5-a023-ec6c9d1156b6","resolution":{"observed_at":"2026-08-06T18:56:33.536570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.974604Z","title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","venue":null,"work_id":"62782ce4-a9e9-4720-8ff3-e6650019a8dd","year":1996},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.541730Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:761b5bea753b67b9f401375ee26ccf7517efbe89ccdbb01b0b04a08dfc2e9641","observation_id":"8b0cee59-1e2b-4b09-b6d1-99ba38c5023d","resolution":{"observed_at":"2026-08-06T18:56:35.981050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.956494Z","title":"Empirical risk minimization under fairness constraints","venue":null,"work_id":"4fc7f4bf-5cec-48ef-8f25-e2d667568b31","year":2018},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.546435Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:db190b8c269c0136073eac24179fc8f5df549d6197c0bd3c985643a92e373ec0","observation_id":"107c988c-9020-4719-8728-a8e9a77daf41","resolution":{"observed_at":"2026-08-06T18:56:35.962326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.940191Z","title":null,"venue":null,"work_id":"0f5c3e98-780a-4ff5-a888-e954d2916e6c","year":2020},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.551204Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:292b97be58bf8ace5b55ae4a9da494c9d547161c138316f3801bdd961022eb8b","observation_id":"ccbee761-50a2-4f48-bc86-edc9136234c1","resolution":{"observed_at":"2026-08-06T18:56:35.945484Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.922700Z","title":null,"venue":null,"work_id":"e23525d2-0210-4688-817b-3225c65151cb","year":2022},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.556654Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f1fa8afd8928348fd58b79a83694b0f5b6cb80ba21b7ad8e026babf0a571e47d","observation_id":"d4fe1d17-780b-4fe6-86ac-95c5ba9f6329","resolution":{"observed_at":"2026-08-06T18:56:35.928467Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.904225Z","title":"Retiring adult: New datasets for fair machine learning","venue":null,"work_id":"9199b213-1e51-4f9e-af15-4fd989d7654c","year":2021},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.561910Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f8f16d547d0333f745be6a0a32b649d689f2f73fd7bc4c437be0a2551d25ac3a","observation_id":"e4f60f7d-737b-4aca-a064-03d11188ddfa","resolution":{"observed_at":"2026-08-06T18:56:35.909497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.873019Z","title":"Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","venue":null,"work_id":"b6069104-d366-4402-9bfb-b7aacd13752f","year":2017},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.566988Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f4c7cba63ec1a741bb91f19069badc94a1a63107a07b3f05ab169cfb63ad6165","observation_id":"93c1d3bb-20a1-428f-aeee-09a50e257f00","resolution":{"observed_at":"2026-08-06T18:56:35.878614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.850019Z","title":"Are sex-based physiological differences the cause of gender bias for chest x-ray diagnosis? In Workshop on Clinical Image-Based Procedures, pages 142--152","venue":null,"work_id":"d238e2df-1f08-4033-9129-d7dd90c7cda3","year":2023},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.571857Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:f4391de75fe71a4575d1d4f2508e2b9ecffa0a50096f1548861a103cb34ce5a6","observation_id":"b404b376-5770-4529-b600-0538964c9763","resolution":{"observed_at":"2026-08-06T18:56:35.856525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.831483Z","title":"Fitzpatrick","venue":null,"work_id":"093befa0-5280-4a5d-879f-b71b96c25b5f","year":1988},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.576299Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:3edb9332f14885776ec3db3ca767dffa1cc2458b892b54883a465fa1439d8517","observation_id":"9d350a22-a8be-46d7-97e0-8e66b96f8c8f","resolution":{"observed_at":"2026-08-06T18:56:35.838236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:56:35.811898Z","title":"Novoa, Justin M","venue":null,"work_id":"2f73a2ea-094f-4823-914e-eaba8f210e7c","year":2017},"citing_paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-06T18:56:33.581348Z"},"links":{"citing_paper":"/paper/2507.08866"},"observation_digest":"sha256:207d2b46be92b81069ae8f9bb995dc800f1fa6828b56edff5bbad42f2eec5e3d","observation_id":"997dc3ef-5de4-418a-b0fd-85d8964fe279","resolution":{"observed_at":"2026-08-06T18:56:35.817975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.08866","last_updated":"2025-07-09T15:52:11Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T17:33:59.087319Z","submitted_at":"2025-07-09T15:52:11Z","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":36,"verified_exact":6,"verified_fuzzy":31},"total_outbound_references":77},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2507.08866."}