Pith. sign in

Paper Citation Record · LEDGER

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond

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.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.08866 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:56:33.581348Z

measured 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

77 of 77 outbound references displayed

  • verified exact6
  • verified fuzzy31
  • unresolved36
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e72a3053-4bda-4461-9eca-559667982cf8 · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.162188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.162188Z digest=sha256:096a98090be21fcf4857305d7f353f9e8bb18a33545a9cec4f9dff03bdb48799

Observation c0b749ea-a438-401b-94e8-4eb0bdffd3d2 · outbound

This paper cites Machine bias.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Machine bias

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.168085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.168085Z digest=sha256:a1c010971ae95d4f7b607163b03ae2777688a58e2f202706b953ca3df4850729

Observation 1afb6d43-e998-40ee-800f-ac226fc431d1 · outbound

This paper cites Dissecting racial bias in an algorithm used to manage the health of populations.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Dissecting racial bias in an algorithm used to manage the health of populations

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.175204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.175204Z digest=sha256:f6ee2683640905928a0ba58b32d84db0ed28df35f75ccd3e85426b1e4081d046

Observation 85d5670c-7728-4248-9658-26ea4bf9e67d · outbound

This paper cites Towards a standard for identifying and managing bias in artificial intelligence.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Towards a standard for identifying and managing bias in artificial intelligence

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.557991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.186421Z digest=sha256:af1cfbe5487cbd6093fcc22c6994a0cce24879fa7a7269b518bd57601d25f17a

Observation 956cde05-af99-4c38-bd69-a373a30a5e92 · outbound

This paper cites Artificial intelligence act.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Artificial intelligence act

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.539963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.192251Z digest=sha256:ee5b3cfc2a34af10f33087273674d237d5f5fcaec7726bdaf9d2a41891fca0ca

Observation 6b196046-8d9c-4052-aea9-7adb288e4bec · outbound

This paper cites Information technology — artificial intelligence (ai) — bias in ai systems and ai aided decision making, 2021.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Information technology — artificial intelligence (ai) — bias in ai systems and ai aided decision making, 2021

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.522318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.202079Z digest=sha256:51603a62944d40d63f6a8f068294dd67807c94dfc25bd03def8cb0483ccad42d

Observation 7bcaca34-dcd0-4034-a699-a615184dc0e6 · outbound

This paper cites Gillis, Vitaly Meursault, and Berk Ustun.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Gillis, Vitaly Meursault, and Berk Ustun

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.209406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.209406Z digest=sha256:3fb6a19812f3b100d9a851afd32761ab84febd9c7bcd6395705c85a45821f407

Observation 0aa2ece0-2128-40ba-98ad-0c3381c61029 · outbound

This paper cites Fairness and bias in algorithmic hiring.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Fairness and bias in algorithmic hiring

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.215428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.215428Z digest=sha256:8fe9548d50c9d22c709222cdfefc889d757cec2c0c4e1b2b8e57403c4e1f2250

Observation c8adf50c-ce05-4f37-b2cf-86a9d04e87de · outbound

This paper cites Baker and Aaron Hawn.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Baker and Aaron Hawn

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.221555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.221555Z digest=sha256:70da079d72d455bfa620ef614b5245b8e826c97c1c50f031bf7d10c0daaed23b

Observation 99541152-c393-4e60-94b9-6b1fd865423b · outbound

This paper cites A data quality approach to the identification of discrimination risk in automated decision making systems.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond A data quality approach to the identification of discrimination risk in automated decision making systems

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.228609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.228609Z digest=sha256:834b0bb74b45e7ca55b719f668759e4c8d91126f8ae0791bf75b68303c18bacd

Observation e4c046b7-f6a0-464d-b1f5-a535b89f2214 · outbound

This paper cites Properties of fairness measures in the context of varying class imbalance and protected group ratios.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Properties of fairness measures in the context of varying class imbalance and protected group ratios

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.502529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.233839Z digest=sha256:6aa612f7fe9af45869c83e9b270724fbb2c30fa471ac306f6b34cdadc28c3878

Observation 29b3d04f-7604-412f-a456-fa2bc4705871 · outbound

This paper cites Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.238797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.238797Z digest=sha256:f3b8ad33718d84a007f821d20f164f9148cedc67ec6182d111a2d0cc22b17531

Observation 194c4f71-ef3a-4977-b443-b7732e8d62e5 · outbound

This paper cites Auditing fairness under unawareness through counterfactual reasoning.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Auditing fairness under unawareness through counterfactual reasoning

Reference 14

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:56:35.616490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.243971Z digest=sha256:950fbf089a6750dd88b7f79363c3ddad0c1432b153e180796e50e2d9b8c06706

Observation a490e68f-1da6-4246-98b6-dca238c07274 · outbound

This paper cites Measuring fairness in credit ratings.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Measuring fairness in credit ratings

Reference 15

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:56:35.516728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.248826Z digest=sha256:378b2cd0f371336d24b5d4857de4f62f64c84e88e8bdd334b8fa0899d9b67810

Observation 9518b093-2121-48c6-9b26-57216cb6fdf1 · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.253516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.253516Z digest=sha256:97387c4cabdcdf99a4986e9b1af4fa74350c66e9cd8d45a41ecc8959abe90fa6

Observation c3dbeaff-9469-4bc4-9f33-745afc860bcb · outbound

This paper cites Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.258589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.258589Z digest=sha256:d31e2fe51eba58bfdd9926d0b4276d07180691847a7566937fdf880d0950a49c

Observation dd0a8bc4-82f1-46c0-bf6c-254f95f81f8d · outbound

This paper cites Long-term fairness with unknown dynamics.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Long-term fairness with unknown dynamics

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.466556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.263683Z digest=sha256:4813caf61751874fc12ee8d89a1df662e9420a87569b23befc5b5dfefcc321a1

Observation bf6c9e6f-679f-448b-824a-b340b3edbc8b · outbound

This paper cites Cruz and Moritz Hardt.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Cruz and Moritz Hardt

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.448339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.268897Z digest=sha256:22671c49a8de40140fa2939d47c54b2d9216a31af41a563783366f8b3a44fe13

Observation c387875d-2103-42a4-9711-343afa3ea7d6 · outbound

This paper cites Learning fair representations via rebalancing graph structure.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Learning fair representations via rebalancing graph structure

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.273873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.273873Z digest=sha256:cd48761f739eafdb43bb93df2290c36ec7c6add0fa23d2f26472ea5848a8e273

Observation 21bf0eed-bec0-4177-b015-926d866c726c · outbound

This paper cites FAL-CUR: fair active learning using uncertainty and representativeness on fair clustering.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond FAL-CUR: fair active learning using uncertainty and representativeness on fair clustering

Reference 21

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:56:35.229950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.280439Z digest=sha256:585db639cdb7663fb0c10b282df2e3ce1579c10600c1d513ec8e489a514f2771

Observation a752d47d-f874-43bc-8f27-8dbdfee8b239 · outbound

This paper cites Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.431178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.285530Z digest=sha256:d722b7efe0e74234914ea300843ddabd38911b6dbdc50c750b9b93de38a4060f

Observation ba840c95-f215-4735-a673-c4f20bd0fe3a · outbound

This paper cites Non-discrimination law in Europe: a primer for non-lawyers.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Non-discrimination law in Europe: a primer for non-lawyers

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:56:35.135438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.290742Z digest=sha256:a0eb0db203c96231b8e93e463e2297e596200e647401f3bbf27c6d4f5fb9e0d8

Observation efdedcc7-e940-4140-803b-c841a6e4ccd4 · outbound

This paper cites Bias on demand: A modelling framework that generates synthetic data with bias.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Bias on demand: A modelling framework that generates synthetic data with bias

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.303310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.303310Z digest=sha256:91a5b9ca8f27a5ebe8fadaebd5d8c057496e63967b132b9838b9fb2a9f573892

Observation a9f2991b-d4f4-45b4-912e-811c5d51364d · outbound

This paper cites On explaining unfairness: An overview.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond On explaining unfairness: An overview

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.309693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.309693Z digest=sha256:1c37e48fb933e42e1e3b8c6a73845b7b75944ed5a47cb110348aae8eff9703b6

Observation 56db4888-b469-49a8-8116-f23bac6f42d1 · outbound

This paper cites Detecting risk of biased output with balance measures.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Detecting risk of biased output with balance measures

Reference 27

Resolution
verified exact
doi, observed 2026-08-06T18:56:33.714861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.316857Z digest=sha256:bd6174e5a6bc34abeaf5f54d8b66a800fb7ededb168fed3602a64bb7c8e2927f

Observation d1969b9c-6e9f-443b-adf7-7d8f5601e82d · outbound

This paper cites Measuring imbalance on intersectional protected attributes and on target variable to forecast unfair classifications.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Measuring imbalance on intersectional protected attributes and on target variable to forecast unfair classifications

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.322203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.322203Z digest=sha256:e6177786e6e7b4c4aad52dff8fe58449741a078ae630984d86d98bed81730748

Observation 9904165b-34ef-4ced-a9bd-a8861f9d1bbb · outbound

This paper cites Wallach, Hal Daum \' e III, and Kate Crawford.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Wallach, Hal Daum \' e III, and Kate Crawford

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.327341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.327341Z digest=sha256:5e8a9dc2132d4bc1376aed6141cf5e88a24c1d88d50ecb25ce68e9b7a46ddf37

Observation b0a6ae34-5731-4ab0-9c7e-dcf83852707e · outbound

This paper cites The dataset nutrition label.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond The dataset nutrition label

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.411700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.332194Z digest=sha256:58359d53889e42e65412fa7487c0aa8e230d38eca8bc258ba9dffb8a3c6d13f0

Observation 44260314-8f1e-4dd7-934a-a631ef058291 · outbound

This paper cites Data cards: Purposeful and transparent dataset documentation for responsible AI.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Data cards: Purposeful and transparent dataset documentation for responsible AI

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.336867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.336867Z digest=sha256:3d6e2324291546512b25e08610cf4c10ecc7e4f56fe4f66f9ba7a03d517dfb24

Observation 55c8bd78-d564-408b-992e-a360686502e7 · outbound

This paper cites Algorithmic fairness datasets: the story so far.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Algorithmic fairness datasets: the story so far

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.341656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.341656Z digest=sha256:78d5c0d04588148380254ea3c14d356bff74b5fd6a70792859af188409409856

Observation 427ae7f1-93f0-4875-a73a-4da5b06a6e7b · outbound

This paper cites Ai documentation: A path to accountability.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Ai documentation: A path to accountability

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.391822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.346778Z digest=sha256:0d2ec43d5ac20c607813490b8040dd126f74b4746edd72779628616cc9b1d425

Observation 41ed07a9-98ba-4c27-a94d-6b0baa361a21 · outbound

This paper cites Completeness of datasets documentation on ML/AI repositories: An empirical investigation.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Completeness of datasets documentation on ML/AI repositories: An empirical investigation

Reference 34

Resolution
verified exact
doi, observed 2026-08-06T18:56:36.374231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.351983Z digest=sha256:0285fad74331f4d0e1647e9e59a1653e837e0a870b224a1438e74fca8a334cdb

Observation d43cf557-b2a9-4dfc-bbaa-1229b68a9f91 · outbound

This paper cites Pandit, Sven Schade, Declan O'Sullivan, and Dave Lewis.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Pandit, Sven Schade, Declan O'Sullivan, and Dave Lewis

Reference 35

Resolution
verified exact
doi, observed 2026-08-06T18:56:36.356580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.357256Z digest=sha256:7899acaf0d34ead3ee60ef550d693f0de20af125f6eccb70d42bbee2e799c321

Observation ea4cbcab-f0e9-4c38-912b-6ab46b7d90ca · outbound

This paper cites everyone wants to do the model work, not the data work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond everyone wants to do the model work, not the data work

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.362305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.362305Z digest=sha256:b4b3d8250021a7153046585596f425ec98f00956bf18f9fed463263334d0cee0

Observation 2f6ab1dd-568e-4016-92c9-cad3aff0bbcb · outbound

This paper cites Metrics for dataset demographic bias: A case study on facial expression recognition.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Metrics for dataset demographic bias: A case study on facial expression recognition

Reference 37

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:56:34.675415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.367213Z digest=sha256:48f4dc8baff9e071b5443c463ff26cc4e9ac3cb965e54878eccca459b82c998b

Observation 44fc2d28-a5b9-4bdc-a34c-9b2869e7b0b9 · outbound

This paper cites A survey on bias and fairness in machine learning.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond A survey on bias and fairness in machine learning

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.372588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.372588Z digest=sha256:2266e7af1c5f191e2f58c6fc4ee88662fa6ef56e10e1611c5edbdc6e70dea51d

Observation 555782d1-d10a-486e-8665-730fd05d2b0d · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.377331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.377331Z digest=sha256:61cc4ccaf474165f38147ba82025f68c94ea02a2861592683d02ed1fdc478778

Observation ce5a5769-d3d0-41c3-b7a9-21b6e4e6a166 · outbound

This paper cites Invisible women: Data bias in a world designed for men.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Invisible women: Data bias in a world designed for men

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.338384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.381986Z digest=sha256:1e74a5697b8bea744378da3970bdf855fabdb11f6f7a5f4ed956d89fa0460f9d

Observation 49864623-99e6-4c72-a755-3108d6e27faa · outbound

This paper cites The uncounted.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond The uncounted

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.318271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.386416Z digest=sha256:64dd5442345845a99e87022354cf93863ad2abf4d6178373e1389293fc8dad21

Observation a8c4fe16-2f7b-4771-be44-9a8348b94958 · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:56:36.298736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.391108Z digest=sha256:bd0bacb58fd42887a9e4036c3437e2ea4fab2c067ddcc69e993c5ef3f58ab42d

Observation c07a1ebe-73c3-4649-90fc-0f0f0415a492 · outbound

This paper cites Gender shades: Intersectional accuracy disparities in commercial gender classification.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Gender shades: Intersectional accuracy disparities in commercial gender classification

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.277876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.395757Z digest=sha256:4c73cb65534b1e2de2e4391d9344dc5e7bef0a2ab4c3db8d2736939479dbb536

Observation 9eab6bab-e7fd-49bf-88d3-41ccf11ad5dc · outbound

This paper cites Fairness and Machine Learning: Limitations and Opportunities.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Fairness and Machine Learning: Limitations and Opportunities

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.253961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.400358Z digest=sha256:86f3cd02412ece6ebef846b44994befc79efa34e597d6354aa054c9b5b543f92

Observation 8d0c5cfc-6b4c-49c8-a10f-ac82964793a1 · outbound

This paper cites The impact of group membership bias on the quality and fairness of exposure in ranking.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond The impact of group membership bias on the quality and fairness of exposure in ranking

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.404877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.404877Z digest=sha256:99b74c7c2cf79ea66a07a3f89f1a0c34979649e8567d75440950e3bdec18bf4f

Observation c2f133b5-5a1a-4823-b68e-8a676dc90431 · outbound

This paper cites It's compaslicated: The messy relationship between RAI datasets and algorithmic fairness benchmarks.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond It's compaslicated: The messy relationship between RAI datasets and algorithmic fairness benchmarks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.232785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.409838Z digest=sha256:e3f92febc77a94a79466d97126f0e90163347783ddbd96fb8f059a60d6d1d2c0

Observation d0de057c-539d-4333-b686-aa05b182e8e0 · outbound

This paper cites Potential biases in machine learning algorithms using electronic health record data.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Potential biases in machine learning algorithms using electronic health record data

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.212075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.414616Z digest=sha256:0c42f231c0d72c05266b968da0e8b88af14080a582fc7adcb3af7fdf0ad84ade

Observation 5ff26288-8a48-4f3c-8ea1-5bcbba4a9261 · outbound

This paper cites Unintended bias and identity terms, 2018.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unintended bias and identity terms, 2018

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.192517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.419313Z digest=sha256:b19f6ccbeb97d12756abc58a3863ec86f4a22e6af571b786f41095074e16d2c2

Observation a2b8762d-ae30-4609-893d-b155eb4df9c5 · outbound

This paper cites Data preprocessing techniques for classification without discrimination.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Data preprocessing techniques for classification without discrimination

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.424038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.424038Z digest=sha256:cd83fb74a00c6c7fecb8558ca9ce527d0f6571bc9e7c515bf25899135227d9e7

Observation 337d8d86-9c19-451b-9a5e-ac2975d67fb7 · outbound

This paper cites Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.429378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.429378Z digest=sha256:84a31a9961f18ea3e114e659ea3b6615f7b970e28c377b6b5eef6f17868e356f

Observation f47241f6-b032-4f70-a910-8a853c1bb9d7 · outbound

This paper cites Aim: Attributing, interpreting, mitigating data unfairness.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Aim: Attributing, interpreting, mitigating data unfairness

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.169259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.439793Z digest=sha256:6a63f2df2e63fb5e37ca605a17b203b4cc4170980ebca479f347ab4ac65b0284

Observation fc144b5e-3b3b-48da-80c5-6e9efbb97aa0 · outbound

This paper cites Jacobs and Hanna Wallach.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Jacobs and Hanna Wallach

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.444334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.444334Z digest=sha256:fabf4d454179664df62270b93d7db1a5b7c2c11fc405314180b9525668106cc6

Observation b3ce40c2-a470-4222-83bf-32cb9c1c95e0 · outbound

This paper cites Comparison and benchmark of name-to-gender inference services.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Comparison and benchmark of name-to-gender inference services

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.449131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.449131Z digest=sha256:74e1003d2a395bdc61a5ef75a64b8fcdf6557ee9de86d4944342d1dc6cc4534a

Observation 1aa31adf-4fe1-4fe0-8c03-160d9d0c3301 · outbound

This paper cites Demographic prediction based on user's browsing behavior.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Demographic prediction based on user's browsing behavior

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.454282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.454282Z digest=sha256:74110aae686068a78225c5d3cb05ea20d9f12e081851eb9d8f12a5938f8e799d

Observation cf3d9bb8-4cbc-4f7c-b97d-d47f88931c29 · outbound

This paper cites Equality of opportunity in supervised learning.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Equality of opportunity in supervised learning

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.151569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.459567Z digest=sha256:5162a73c77d08e4029b013245ce3e595f9e35ee50c4caea4597b294b6614e675

Observation d0966b76-9764-4bda-8c2f-3b8a99ba990a · outbound

This paper cites Discrimination-aware data mining.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Discrimination-aware data mining

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.464337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.464337Z digest=sha256:860e049dc926032257b373fcfe197833d64de4c23845829b1a21589a19002b7b

Observation 3b91d61b-8551-4b7b-a768-b92a38053ef6 · outbound

This paper cites Big data's disparate impact.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Big data's disparate impact

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.469229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.469229Z digest=sha256:8d3ee695dd3c592f1fcad75bea999ee698c3b163ff477794ce8a1d95de1b21bf

Observation 42a17f74-cdc1-4c19-98e4-0ddc0d4d323f · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 59

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:56:36.118287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.474262Z digest=sha256:f65c1f7ba27a0a17c5a089e7600fe0c6e83461121ef1f8e8cf0eeacfb63c6189

Observation b2a883b0-b61c-4383-9407-2f3f2c30b7f4 · outbound

This paper cites Censoring Representations with an Adversary.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Censoring Representations with an Adversary

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.479764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.479764Z digest=sha256:71114bbd95769da0b055f3adae88285a7b66965b21c0b97072735f38c73ddadf

Observation 8995bb02-8a8a-481c-9d39-1dc0b4f1354a · outbound

This paper cites Reducing unintended bias of ML models on tabular and textual data.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Reducing unintended bias of ML models on tabular and textual data

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.484957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.484957Z digest=sha256:8ea8e40d121b3246be47e37c98d50afcf1541b694f917b8d5bcfac4684272732

Observation 76f6fd39-57cb-47bf-87aa-976b7c79f2b1 · outbound

This paper cites Explainability statement, 2022.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Explainability statement, 2022

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.099939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.489488Z digest=sha256:ccd6c3d6908f11ae45002d5fd33ef9617e452bdc12f04f636544e972c273d665

Observation 8ba76ff5-1644-41e0-aa51-4d2c6294578f · outbound

This paper cites Unbiased interviews.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unbiased interviews

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.080537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.494104Z digest=sha256:aabb84da0bf0d09770bb378a81d30757a9e812ec65364f3039d719db17ecb99a

Observation a0ca6e6c-8f0a-401b-9b4f-8b37c4e30d43 · outbound

This paper cites Navigating demographic measurement for fairness and equity.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Navigating demographic measurement for fairness and equity

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.056979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.498746Z digest=sha256:186234e9ede67e51ed7efcec5171f6f27a02b46575d626cff6add7592fc5678c

Observation e0592bc5-3e2d-443b-b33b-27e521e572ef · outbound

This paper cites Chen, and Marzyeh Ghassemi.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Chen, and Marzyeh Ghassemi

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.037449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.503385Z digest=sha256:a6ff97527e98ee7a880f1a0f918746b8c6f77d1a4b67fd6d37cd989ccab42652

Observation 8c059e37-3376-461b-888c-036b53ff8f98 · outbound

This paper cites Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.020840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.507785Z digest=sha256:030767480f2866af9a417666c78a1bd69d5bb3fa081f89575230f738ba0774a2

Observation 24e7eaad-a5b0-45b8-9012-b416752fee46 · outbound

This paper cites Measuring discrimination in algorithmic decision making.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Measuring discrimination in algorithmic decision making

Reference 67

Resolution
verified exact
doi, observed 2026-08-06T18:56:33.641094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.512896Z digest=sha256:5b1c37fe96ec4dda961bdd47198495648cef7280b55873bffcc01e712e5c64b4

Observation 2bc126db-e673-4231-be5a-4135f52387a9 · outbound

This paper cites Fairness in deep learning: A computational perspective.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Fairness in deep learning: A computational perspective

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.519397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.519397Z digest=sha256:9a33793fdf7b386e9596380950860327fd4166db1047baf7257ac9173a12b02f

Observation 778f13f1-380c-40f6-abec-ad4edc551214 · outbound

This paper cites On formalizing fairness in prediction with machine learning, 2018.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond On formalizing fairness in prediction with machine learning, 2018

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:36.001471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.525826Z digest=sha256:7a88a65ce7dddf07e1ea5e738dc9da3b2196252380d3209e7908672d58a90944

Observation b19541a6-db33-4170-95ec-650a8398e7f6 · outbound

This paper cites The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric

Reference 70

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:56:33.776512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.531464Z digest=sha256:f6d7c6a697f3c2bd0452c8be6cb03f49575dcae1b89a848bcb1580b7ad2dc2c5

Observation 7000e5eb-d0d2-4ce5-a023-ec6c9d1156b6 · outbound

This paper cites Zhang, Mark Harman, and Federica Sarro.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Zhang, Mark Harman, and Federica Sarro

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-06T18:56:33.536570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:56:33.536570Z digest=sha256:2634af193e1988c4b1a6dede6f2bf8d566396f1f9d19e056b21434cafd7cd521

Observation 8b0cee59-1e2b-4b09-b6d1-99ba38c5023d · outbound

This paper cites Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.981050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.541730Z digest=sha256:761b5bea753b67b9f401375ee26ccf7517efbe89ccdbb01b0b04a08dfc2e9641

Observation 107c988c-9020-4719-8728-a8e9a77daf41 · outbound

This paper cites Empirical risk minimization under fairness constraints.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Empirical risk minimization under fairness constraints

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.962326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.546435Z digest=sha256:db190b8c269c0136073eac24179fc8f5df549d6197c0bd3c985643a92e373ec0

Observation ccbee761-50a2-4f48-bc86-edc9136234c1 · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 74

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:56:35.945484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.551204Z digest=sha256:292b97be58bf8ace5b55ae4a9da494c9d547161c138316f3801bdd961022eb8b

Observation d4fe1d17-780b-4fe6-86ac-95c5ba9f6329 · outbound

This paper cites an unresolved cited work.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Unresolved cited work

Reference 75

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:56:35.928467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.556654Z digest=sha256:f1fa8afd8928348fd58b79a83694b0f5b6cb80ba21b7ad8e026babf0a571e47d

Observation e4f60f7d-737b-4aca-a064-03d11188ddfa · outbound

This paper cites Retiring adult: New datasets for fair machine learning.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Retiring adult: New datasets for fair machine learning

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.909497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.561910Z digest=sha256:f8f16d547d0333f745be6a0a32b649d689f2f73fd7bc4c437be0a2551d25ac3a

Observation 93c1d3bb-20a1-428f-aeee-09a50e257f00 · outbound

This paper cites Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.878614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.566988Z digest=sha256:f4c7cba63ec1a741bb91f19069badc94a1a63107a07b3f05ab169cfb63ad6165

Observation b404b376-5770-4529-b600-0538964c9763 · outbound

This paper cites 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.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond 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

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.856525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.571857Z digest=sha256:f4391de75fe71a4575d1d4f2508e2b9ecffa0a50096f1548861a103cb34ce5a6

Observation 9d350a22-a8be-46d7-97e0-8e66b96f8c8f · outbound

This paper cites Fitzpatrick.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Fitzpatrick

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.838236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.576299Z digest=sha256:3edb9332f14885776ec3db3ca767dffa1cc2458b892b54883a465fa1439d8517

Observation 997dc3ef-5de4-418a-b0fd-85d8964fe279 · outbound

This paper cites Novoa, Justin M.

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond Novoa, Justin M

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:56:35.817975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.581348Z digest=sha256:207d2b46be92b81069ae8f9bb995dc800f1fa6828b56edff5bbad42f2eec5e3d

Pith citing papers

No inbound Pith citation observations are available.