Paper Citation Record · LEDGER

Are generative models fair? A study of racial bias in dermatological image generation

As of 15 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2501.11752.

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

pith.paper-citation-record.v1
2501.11752 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:57:06.680066Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

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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

25 of 25 outbound references displayed

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External citation measurements

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Outbound references

Observation 5cf313e3-d0c5-483f-a743-7125f482e473 · outbound

This paper cites In: 2023 IEEE 6th International Conference on Multimedia Information Processing and Retrieval (MIPR).

Are generative models fair? A study of racial bias in dermatological image generation In: 2023 IEEE 6th International Conference on Multimedia Information Processing and Retrieval (MIPR)

Reference 1

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Observation 8ee39dd6-e9b5-4083-993d-da475580e090 · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2024).

Are generative models fair? A study of racial bias in dermatological image generation In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2024)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.428388Z

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Observation 2d149d4e-5a77-4f8d-87fc-9e25df8e5c07 · outbound

This paper cites Science advances 8(31), eabq6147 (2022).

Are generative models fair? A study of racial bias in dermatological image generation Science advances 8(31), eabq6147 (2022)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.408276Z

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Observation 773ac585-decd-4a49-b564-4a46dd904141 · outbound

This paper cites Academic Emergency Medicine24(8), 895–904 (2017).

Are generative models fair? A study of racial bias in dermatological image generation Academic Emergency Medicine24(8), 895–904 (2017)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.384826Z

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Observation 9f1bc21b-da6f-4082-a1cf-7f64e16fa952 · outbound

This paper cites The Lancet Digital Health 4(6), e406–e414 (2022).

Are generative models fair? A study of racial bias in dermatological image generation The Lancet Digital Health 4(6), e406–e414 (2022)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.360174Z

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Observation e79702b8-9eeb-4d6c-83b8-91c0bce55fbd · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention.

Are generative models fair? A study of racial bias in dermatological image generation In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Reference 6

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Observation a03da04e-2c84-4658-9bd2-d57991f7824f · outbound

This paper cites Nature Medicine30(2), 573–583 (2024).

Are generative models fair? A study of racial bias in dermatological image generation Nature Medicine30(2), 573–583 (2024)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.314178Z

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Observation fac16757-5091-410f-8c69-af4ef215ba21 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Are generative models fair? A study of racial bias in dermatological image generation In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.291170Z

Source-reported events for the cited work

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Observation 7c28cae2-f6d9-40dc-8266-1bbffe105442 · outbound

This paper cites In: 2017 IEEE winter conference on applications of computer vision (WACV).

Are generative models fair? A study of racial bias in dermatological image generation In: 2017 IEEE winter conference on applications of computer vision (WACV)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.264370Z

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.

Observation 38c2d756-972f-4b25-bb7a-72859bd75d8d · outbound

This paper cites Advances in neural information processing systems 31 (2018).

Are generative models fair? A study of racial bias in dermatological image generation Advances in neural information processing systems 31 (2018)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.242335Z

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.

Observation d8cf572c-be53-4b47-91a6-4576951164e0 · outbound

This paper cites In: Workshop on Clinical Image-Based Procedures.

Are generative models fair? A study of racial bias in dermatological image generation In: Workshop on Clinical Image-Based Procedures

Reference 11

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Observation 63461878-7412-44e5-a775-03bdf1d4c08e · outbound

This paper cites López-Pérez et al.

Are generative models fair? A study of racial bias in dermatological image generation López-Pérez et al

Reference 12

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Observation 7e2cda5b-629a-442f-b6eb-315033b98f6f · outbound

This paper cites In: Inter- national Conference on Medical Image Computing and Computer-Assisted Inter- vention.

Are generative models fair? A study of racial bias in dermatological image generation In: Inter- national Conference on Medical Image Computing and Computer-Assisted Inter- vention

Reference 13

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Observation f2ef5187-f369-4816-bd99-6efad8639ea6 · outbound

This paper cites Proceedings of the National Academy of Sciences117(23), 12592–12594 (2020).

Are generative models fair? A study of racial bias in dermatological image generation Proceedings of the National Academy of Sciences117(23), 12592–12594 (2020)

Reference 14

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Observation 13ae2f31-9897-45b7-ab36-0b03e7b0cc62 · outbound

This paper cites ACM Computing Surveys55(9), 1–46 (2023).

Are generative models fair? A study of racial bias in dermatological image generation ACM Computing Surveys55(9), 1–46 (2023)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.130042Z

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Observation baa27817-e02e-4dd7-ad0d-0ae1ca7d6b0d · outbound

This paper cites In: Proceedings of the ACM conference on health, inference, and learning.

Are generative models fair? A study of racial bias in dermatological image generation In: Proceedings of the ACM conference on health, inference, and learning

Reference 16

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Observation 73904f87-22ac-4c4f-a0c0-5a3747cb7858 · outbound

This paper cites In: MICCAI Workshop onFairnessof AIin Medical Imaging.pp.

Are generative models fair? A study of racial bias in dermatological image generation In: MICCAI Workshop onFairnessof AIin Medical Imaging.pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.091860Z

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

Observation 367aa16b-5894-45a2-a262-029d2b3c0868 · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention.

Are generative models fair? A study of racial bias in dermatological image generation In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Reference 18

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Observation f185b963-d394-4464-8af4-02c130de1a82 · outbound

This paper cites Patterns4(7) (2023).

Are generative models fair? A study of racial bias in dermatological image generation Patterns4(7) (2023)

Reference 19

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

Observation 46c3965e-c0ca-4b36-8ba2-1d30788df7e1 · outbound

This paper cites In: Xing, E.P., Jebara, T.

Are generative models fair? A study of racial bias in dermatological image generation In: Xing, E.P., Jebara, T

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:07.012639Z

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Observation 29df11e6-7b9d-44d8-a150-367993f9a76e · outbound

This paper cites In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022).

Are generative models fair? A study of racial bias in dermatological image generation In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:06.983627Z

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

Observation 4b757258-33de-4b28-a2d6-28987db9ec43 · outbound

This paper cites Advances in Neural Information Processing Systems36 (2024).

Are generative models fair? A study of racial bias in dermatological image generation Advances in Neural Information Processing Systems36 (2024)

Reference 22

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Observation 067dec5d-35cf-4c5f-9dab-69bbb3f5e918 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Are generative models fair? A study of racial bias in dermatological image generation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 23

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Observation 9e1a2703-1fe1-451a-a94d-3573819d9fae · outbound

This paper cites Jama 314(6), 555–556 (2015).

Are generative models fair? A study of racial bias in dermatological image generation Jama 314(6), 555–556 (2015)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:57:06.922127Z

Source-reported events for the cited work

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Observation 3cd59310-6466-4c97-a5c7-632264a80c4a · outbound

This paper cites RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR.

Are generative models fair? A study of racial bias in dermatological image generation RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T17:57:06.680066Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.