Paper Citation Record · LEDGER

Evaluating and comparing gender bias across four text-to-image models

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2509.08004.

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

pith.paper-citation-record.v1
2509.08004 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:54:51.604116Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 741f4894-1667-4152-9dc6-719077f88e78 · outbound

This paper cites Improving Image Generation with Better Captions.

Evaluating and comparing gender bias across four text-to-image models Improving Image Generation with Better Captions

Reference 1

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Source-reported events for the cited work

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Observation 18cb5a29-d4c5-4f5c-a9c3-fef0ccf826bc · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Evaluating and comparing gender bias across four text-to-image models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 2

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Source-reported events for the cited work

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Observation 93719e71-d172-4eb4-8a75-bebb3e44b89d · outbound

This paper cites Computer-Generated Inclusivity: Fashion Turns to ‘diverse’ Ai Models.

Evaluating and comparing gender bias across four text-to-image models Computer-Generated Inclusivity: Fashion Turns to ‘diverse’ Ai Models

Reference 3

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Observation 1c347479-5f2a-454a-82cc-4c62a3638ddf · outbound

This paper cites Examining Algorithmic Bias in AL-Powered Credit Scoring: Implications for Stakeholders and Public Perception in an EU Country.

Evaluating and comparing gender bias across four text-to-image models Examining Algorithmic Bias in AL-Powered Credit Scoring: Implications for Stakeholders and Public Perception in an EU Country

Reference 4

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Observation 8e66b5b4-51cf-49d2-8831-f424f77741ba · outbound

This paper cites A Comprehensive Study on Bias in Artificial Intelligence Systems.

Evaluating and comparing gender bias across four text-to-image models A Comprehensive Study on Bias in Artificial Intelligence Systems

Reference 5

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Observation 7cd3aa34-c220-4367-937c-06f55035bfc1 · outbound

This paper cites Gender bias in generative artificial intelligence text-to-image depiction of medical students.

Evaluating and comparing gender bias across four text-to-image models Gender bias in generative artificial intelligence text-to-image depiction of medical students

Reference 6

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Observation d383d883-6611-447c-a5b2-a1d4ccf6051e · outbound

This paper cites Identifying Race and Gender Bias in Stable Diffusion AI Image Generation,.

Evaluating and comparing gender bias across four text-to-image models Identifying Race and Gender Bias in Stable Diffusion AI Image Generation,

Reference 8

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Source-reported events for the cited work

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Observation 7f075512-e7b5-486d-8e04-511f46599fd0 · outbound

This paper cites T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation.

Evaluating and comparing gender bias across four text-to-image models T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation

Reference 9

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Observation 3b6f6a13-a282-4e36-a0cd-c6a4ab6a7eb8 · outbound

This paper cites Implications of Identity in AI: Creators, Creations, and Consequences.

Evaluating and comparing gender bias across four text-to-image models Implications of Identity in AI: Creators, Creations, and Consequences

Reference 10

Resolution: verified exact; doi, observed 2026-08-04T23:54:51.769964Z

Source-reported events for the cited work

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Observation a100c2eb-749a-4d50-b924-35f79396dae6 · outbound

This paper cites Holistic Evaluation of Text-To-Image Models.

Evaluating and comparing gender bias across four text-to-image models Holistic Evaluation of Text-To-Image Models

Reference 11

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Source-reported events for the cited work

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Observation 660038c2-ed1e-418e-b195-df5f12d00fcf · outbound

This paper cites DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models.

Evaluating and comparing gender bias across four text-to-image models DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models

Reference 12

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Source-reported events for the cited work

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Observation e9a32994-8d31-472a-bd33-eef15a68d23e · outbound

This paper cites Stable Bias: Analyzing Societal Representations in Diffusion Models.

Evaluating and comparing gender bias across four text-to-image models Stable Bias: Analyzing Societal Representations in Diffusion Models

Reference 13

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Source-reported events for the cited work

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This paper cites Dall-E 3.

Evaluating and comparing gender bias across four text-to-image models Dall-E 3

Reference 14

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Observation 1d7fcb18-106f-42a1-b719-0c0774f47f49 · outbound

This paper cites Stable Diffusion XL.

Evaluating and comparing gender bias across four text-to-image models Stable Diffusion XL

Reference 16

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Observation 2d460d22-d601-4e8c-99ff-7d0e40eb8638 · outbound

This paper cites Stable Cascade.

Evaluating and comparing gender bias across four text-to-image models Stable Cascade

Reference 17

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Observation 96dc5e5d-480f-4346-86a7-82a7c6d8133a · outbound

This paper cites A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health.

Evaluating and comparing gender bias across four text-to-image models A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

Reference 18

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Observation 4536734c-3437-45e4-ae82-052d99ff1c46 · outbound

This paper cites U.S. Senate Aritifcal Intelligence Insight Forum.

Evaluating and comparing gender bias across four text-to-image models U.S. Senate Aritifcal Intelligence Insight Forum

Reference 19

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Source-reported events for the cited work

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Observation 63d2cd24-fffd-4a65-8498-160ea886cbbb · outbound

This paper cites Meta's AI is accused of being RACIST: Shocked users say Mark Zuckerberg's chatbot refuses to imagine an Asian man with a white woman.

Evaluating and comparing gender bias across four text-to-image models Meta's AI is accused of being RACIST: Shocked users say Mark Zuckerberg's chatbot refuses to imagine an Asian man with a white woman

Reference 20

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Observation d620c1e4-d0e8-4112-bb2f-da360a6e553f · outbound

This paper cites We ‘messed up’ with Black Nazi Blunder, Google Co-Founder Admits.

Evaluating and comparing gender bias across four text-to-image models We ‘messed up’ with Black Nazi Blunder, Google Co-Founder Admits

Reference 21

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Observation 9e1fcee4-85cd-4597-9198-a26ad53219a8 · outbound

This paper cites Google Suspends Gemini AI Chatbot’s Ability to Generate Pictures of People.

Evaluating and comparing gender bias across four text-to-image models Google Suspends Gemini AI Chatbot’s Ability to Generate Pictures of People

Reference 22

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Observation f27534cf-bdd5-433a-9ae4-6be9537c0e17 · outbound

This paper cites Humans Are Biased. Generative AI Is Even Worse.

Evaluating and comparing gender bias across four text-to-image models Humans Are Biased. Generative AI Is Even Worse

Reference 23

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Observation ea942fc4-9e00-4e31-8f89-63c7996e7a5d · outbound

This paper cites ImageNet: A large-scale hierarchical image database,.

Evaluating and comparing gender bias across four text-to-image models ImageNet: A large-scale hierarchical image database,

Reference 24

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This paper cites Improving Diffusion Models as an Alternative to Gans, Part 1.

Evaluating and comparing gender bias across four text-to-image models Improving Diffusion Models as an Alternative to Gans, Part 1

Reference 25

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Observation ce30c667-26dc-4c5b-af72-3eda435c5f92 · outbound

This paper cites Accessed 14 May 2024.

Evaluating and comparing gender bias across four text-to-image models Accessed 14 May 2024

Reference 26

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Observation f3f7ea0d-89a2-42b7-b04d-5f50470b6544 · outbound

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Evaluating and comparing gender bias across four text-to-image models Common Crawl - Open Repository of Web Crawl Data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:54:52.111556Z

Source-reported events for the cited work

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Observation 77748551-59b5-4a82-8e92-208124829ac6 · outbound

This paper cites U-Net Convolutional Networks for Biomedical Image Segmentation.

Evaluating and comparing gender bias across four text-to-image models U-Net Convolutional Networks for Biomedical Image Segmentation

Reference 28

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Evaluating and comparing gender bias across four text-to-image models Stable Diffusion with Diffusers

Reference 29

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Observation b3ba427d-aad1-4d32-8bdc-5815c16e4e76 · outbound

This paper cites Official Code for Stable Cascade.

Evaluating and comparing gender bias across four text-to-image models Official Code for Stable Cascade

Reference 30

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Observation b9d51abb-88b5-47eb-bf2c-6eaba7ca5ab9 · outbound

This paper cites Global Gender Gap Report 2020.

Evaluating and comparing gender bias across four text-to-image models Global Gender Gap Report 2020

Reference 31

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Evaluating and comparing gender bias across four text-to-image models Women airline pilots: numbers are growing, but still a pitiful percentage

Reference 32

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Evaluating and comparing gender bias across four text-to-image models Astronaut Fact Book

Reference 33

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Evaluating and comparing gender bias across four text-to-image models Women in Science

Reference 34

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Observation a24ea97e-3af6-4b98-a53f-53a88ed41a43 · outbound

This paper cites Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies, Social Science Research Network.

Evaluating and comparing gender bias across four text-to-image models Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies, Social Science Research Network

Reference 35

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Evaluating and comparing gender bias across four text-to-image models Bias in Artificial Intelligence Algorithms and Recommendations for Mitigation

Reference 36

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Observation abdc3e66-ce5e-40e9-a2af-8c6070d70262 · outbound

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Evaluating and comparing gender bias across four text-to-image models Online images amplify gender bias

Reference 37

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Observation 1349deca-1580-4e04-a5ff-cdffe2768cbc · outbound

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Evaluating and comparing gender bias across four text-to-image models Gender Diversity Crisis in AI

Reference 38

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This paper cites AI and the quest for diversity and inclusion: a systematic literature review.

Evaluating and comparing gender bias across four text-to-image models AI and the quest for diversity and inclusion: a systematic literature review

Reference 39

Resolution: verified exact; doi, observed 2026-08-04T23:54:51.673091Z

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Observation 1af979b9-d53f-4bc8-9a69-aa420d11c53f · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Evaluating and comparing gender bias across four text-to-image models Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 40

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This paper cites Gender differences in individual variation in academic grades fail to fit expected patterns for STEM.

Evaluating and comparing gender bias across four text-to-image models Gender differences in individual variation in academic grades fail to fit expected patterns for STEM

Reference 41

Resolution: verified exact; doi, observed 2026-08-04T23:54:51.661657Z

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Observation 4a15fddb-4a85-4191-817f-69142e0cd2d2 · outbound

This paper cites Perceptions of stereotypes applied to women who publicly communicate their STEM work.

Evaluating and comparing gender bias across four text-to-image models Perceptions of stereotypes applied to women who publicly communicate their STEM work

Reference 42

Resolution: verified exact; doi, observed 2026-08-04T23:54:51.649670Z

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Observation 8b8f9dc6-71cb-440e-8208-fb22337d92ca · outbound

This paper cites GitHub - Zoyahammad/BiasResearch: This Repository Contains Python Scripts Used for SC, SDXL, DALL-E 3.

Evaluating and comparing gender bias across four text-to-image models GitHub - Zoyahammad/BiasResearch: This Repository Contains Python Scripts Used for SC, SDXL, DALL-E 3

Reference 43

Resolution: verified exact; doi, observed 2026-08-04T23:54:51.637977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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