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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 2026-08-15T06:32:42.880941+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

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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

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

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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

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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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.186421Z digest=sha256:660ea1b401d499d0f296de8850d35532a77fae1b33e2c83e538eb85680b09caf

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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.192251Z digest=sha256:e801744ba4dc4220b3731ea99711f89a01f7429485516afec9c6e7d06f02ce47

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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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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

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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

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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

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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

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.233839Z digest=sha256:bbb0d1042bca56e85fa603b287adc72cc7fb12e7b85acadc6484ccf07167e271

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

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.243971Z digest=sha256:31b84e9914fd4492401fc086eeab5f77e412dc6154bf0b1c133a817bb21affda

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.248826Z digest=sha256:356591bb67270c7456ac9601f5f2191e0c575288ac4a7b8a207731650108d177

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

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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

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.263683Z digest=sha256:47e30193b308cebe35c622cddfc5bfaa4f00daaa2d53fa560806e97d831bbd96

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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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

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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

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metadata mismatch
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.285530Z digest=sha256:8fc0726d0130087d323bf55636c1de872b26d6913e6796d0b68cb423222a8bec

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

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

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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

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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

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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 2026-08-15T06:32:42.880941+00:00.

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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

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

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Unavailable: canonical work link unavailable.

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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

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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

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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 2026-08-15T06:32:42.880941+00:00.

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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

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

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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

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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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.346778Z digest=sha256:882f2b8ffb707101aebc089f2fd5dea4ebd74f5dd73eeb43cb416869b58f4776

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.351983Z digest=sha256:365a0f1206d1f6f6c4a6e104d6f85631b9654813db1cebb93c160286febbde3c

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.357256Z digest=sha256:cf84e77845e02f1921e12b7d15e11e3e5c9407f2376054a7f0208f53ee9f1e52

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

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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

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metadata mismatch
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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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

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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

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.381986Z digest=sha256:fbf2a3f6fc19c42c02fe09a59ebe1aa179a94c24ab2b28aefb9c2c3cd08a298a

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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.386416Z digest=sha256:d01cfa7455b5460cdffa8f0c042bab2ad4e5fef76b2645b058a7221e85f9d173

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.391108Z digest=sha256:55a9984c894d5f964bf44b1c1392a02d8599ab5393b45f834d06d1c2eb3bd679

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.395757Z digest=sha256:5c788c94f5ebdee26e23646e1c7c65edd832f6fd0dacdd05c5955592d967d605

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.400358Z digest=sha256:72c0ab4b1f6a5851cde951b853dbebdc0e842e0f336dbc5724f1345eb64117ba

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.409838Z digest=sha256:7d9718e0f2ea0ee7dc8ad8f056d123d4bcb3c11993c72de3b3c50e2b109cd841

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.414616Z digest=sha256:aab71569d580de45e2fa7ce474b8654561040b8dfa2b446143c4e1cf598e1ca3

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.419313Z digest=sha256:06fa4c813f7ee86fcaac2c01c4e522d5543a849f13b37098947ebc39f2e14f35

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.439793Z digest=sha256:6f10608907a4cdcf38b509422b3f7365d53165f3edef6c73a114c7bdd04d9dd9

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.459567Z digest=sha256:cf7943b5412bdf3480dcb32577abe9ff0af94ce846300875bf8f9cd1469486d6

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.474262Z digest=sha256:cef8f3ebbb0b2969c277369676dde6158d5ee28a8885a3012d27b10b4ade861e

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

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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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.489488Z digest=sha256:129320782eace376ebc0757dd7d263f4ddbac1b0eec6fb01d239f99b9d0318cf

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.494104Z digest=sha256:8cfc65d9286ae5724603d373fc5bc40729af8b7658170ad85fbb1f86e0907385

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.498746Z digest=sha256:c6e82ef45ce3d703fc04d210b5d59f041a3d3e5a0ecf8de23314d0472407b25f

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.503385Z digest=sha256:59aff6e2addc49a85bfdc01c200d32a8c3eaf408cd12e76f32069d1b80df91f2

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.507785Z digest=sha256:83e405a33d24029ebb6c4e77f3ccade838b3976440890928bd3f706b64edf4d4

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.512896Z digest=sha256:8514a97f27557866c7ddd0d8e1c87f4a003d03c33864505c790126919e1524c2

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.525826Z digest=sha256:43f8a3f48dd07bce0ce4a220ded8ecee85c36f9471ee782e8f3785cc3f284125

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.531464Z digest=sha256:76eb47b619e1d6819c657834ad2bee1c6db40f0928702670483feae455c77782

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.541730Z digest=sha256:83128fc92763d0481a59bd3ab9b055e45cc575804d0f54dce2c0c72a83b7e389

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.546435Z digest=sha256:7c575b6df7496f67758e0d5c95c11037ea0dc1c8a539560300ce2d7775852f54

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.551204Z digest=sha256:a552077829966dd7dddd9b454c8c167baa0eed1be61ad2a8568852487da4cd95

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.556654Z digest=sha256:ff91d99e6c3e36023a7dc70036ace71ca7860039f2b6ea08e37bec6507008c98

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.561910Z digest=sha256:d8d86f10525dcd0af9b03e97b579dd2123cd8236cbb42f9d3bb55fbbec82888f

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.566988Z digest=sha256:2feb7c5a96212f321cd58ca3857012844d81ad75f3e476cbaa7ae01399ea1eeb

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.571857Z digest=sha256:dfc9b5ea50530c71b64b11d47b2c05938d5a1be5051106be2f3c481bc712ed9b

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.576299Z digest=sha256:69939b15e81d7b1b61e544509bcbfdfdf913902f67ca1b7fff2e435bff1fdfc5

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T18:56:33.581348Z digest=sha256:49f09311d178df9fc6406587dcd259ef420ce0556fc7092450585d30d6c4ae86

Pith citing papers

No inbound Pith citation observations are available.