Paper Citation Record · LEDGER

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2509.00849.

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

pith.paper-citation-record.v1
2509.00849 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:11:56.942952Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 29007dcc-c4bb-449a-9be6-d2e7ab0bd35b · outbound

This paper cites Stereotyping and evaluation in implicit race bias: evidence for independent constructs and unique effects on behavior.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Stereotyping and evaluation in implicit race bias: evidence for independent constructs and unique effects on behavior

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.867767Z

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Observation 1d022a6a-5288-476b-afef-66bdebf44a5b · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2

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Observation aa1649e5-3336-4b49-9902-c86ffc703478 · outbound

This paper cites Improving image generation with better captions.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Improving image generation with better captions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.852031Z

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Observation 6e0f27e5-db21-4270-839f-fcc782e4dbfc · outbound

This paper cites Gender shades: Intersectional accuracy disparities in com- mercial gender classification.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Gender shades: Intersectional accuracy disparities in com- mercial gender classification

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.837619Z

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Observation cf7d001a-8299-4717-b21c-d9fa59f036bd · outbound

This paper cites Biasmap: Can cross-attention uncover hidden social biases? In Workshop on Demographic Diversity in Computer Vision@ CVPR 2025.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Biasmap: Can cross-attention uncover hidden social biases? In Workshop on Demographic Diversity in Computer Vision@ CVPR 2025

Reference 5

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Source-reported events for the cited work

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Observation eb65f621-71a0-45be-8112-9931646f7f55 · outbound

This paper cites TIBET: Identifying and evaluating biases in text-to-image generative models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations TIBET: Identifying and evaluating biases in text-to-image generative models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.806245Z

Source-reported events for the cited work

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Observation 78c77ccb-cdfc-44c7-ad09-3cba077510da · outbound

This paper cites DALL-Eval: Probing the reasoning skills and social biases of text-to-image generation models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations DALL-Eval: Probing the reasoning skills and social biases of text-to-image generation models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.789238Z

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Observation f0a42803-60d9-427b-8db8-a422ea756fdd · outbound

This paper cites Precisedebias: An automatic prompt engi- neering approach for generative ai to mitigate image demographic biases.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Precisedebias: An automatic prompt engi- neering approach for generative ai to mitigate image demographic biases

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.771153Z

Source-reported events for the cited work

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Observation 09b1a1e9-8e71-4a7d-a280-391dce13c256 · outbound

This paper cites Eagly and Steven J.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Eagly and Steven J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.755843Z

Source-reported events for the cited work

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Observation 8a375c92-bc5b-4a5f-88ee-fe1385015be4 · outbound

This paper cites Auditing and instructing text-to-image generation models for fairness.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Auditing and instructing text-to-image generation models for fairness

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.740661Z

Source-reported events for the cited work

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Observation fb0a27b8-b2c4-448d-b058-52dacd2c48f8 · outbound

This paper cites Google gemini imagen 4: Ai image generation.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Google gemini imagen 4: Ai image generation

Reference 11

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Source-reported events for the cited work

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Observation 25005ba1-092f-4dfa-8104-cc8fd016e89b · outbound

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Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation b74f7dde-f6e9-4621-b899-15b48f6e5b97 · outbound

This paper cites Gender and racial bias in visual question answering datasets.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Gender and racial bias in visual question answering datasets

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.691700Z

Source-reported events for the cited work

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Observation 95b37f29-ed28-42de-9b90-24c22d8b3bf6 · outbound

This paper cites Social biases in nlp models as barriers for persons with disabilities.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Social biases in nlp models as barriers for persons with disabilities

Reference 14

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Observation d5a3c4df-dba2-46b1-9931-e3218baf1ab6 · outbound

This paper cites Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.659472Z

Source-reported events for the cited work

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Observation 8d1aa4ac-d3d1-453f-bbb6-b1b1e47ad5d1 · outbound

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Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Unresolved cited work

Reference 16

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Source-reported events for the cited work

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Observation b9051ede-29fb-4443-8921-84f5cdc4423e · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Aligning Text-to-Image Models using Human Feedback

Reference 17

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Source-reported events for the cited work

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Observation 64506883-bb94-4756-bdbf-dc8c5ff9e652 · outbound

This paper cites Holistic evaluation of text-to-image models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Holistic evaluation of text-to-image models

Reference 18

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Source-reported events for the cited work

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Observation 39db6372-4e2a-4a7e-9597-c927553a01cd · outbound

This paper cites Fair Text-to-Image Diffusion via Fair Mapping.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Fair Text-to-Image Diffusion via Fair Mapping

Reference 19

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Source-reported events for the cited work

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Observation bebf24cf-15d5-44a5-93d8-4402ae686168 · outbound

This paper cites Sport–gender stereotypes and their impact on impression evaluations.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Sport–gender stereotypes and their impact on impression evaluations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.611097Z

Source-reported events for the cited work

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Observation 9f5b7aaf-a0e7-4479-9d1f-a83d9f7f26ed · outbound

This paper cites Semi-Automated Segmentation of Geoscientific Data Using Superpixels.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Semi-Automated Segmentation of Geoscientific Data Using Superpixels

Reference 21

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Observation 37819cd6-8421-4c6f-9a39-ed02979a51ca · outbound

This paper cites Relevancy and Diversity in News Recommendations.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Relevancy and Diversity in News Recommendations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.595452Z

Source-reported events for the cited work

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Observation fe78385a-555b-4c68-b94a-839e57aa6826 · outbound

This paper cites Who is responsible? the data, models, users or regulations? responsible generative ai for a sustainable future.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Who is responsible? the data, models, users or regulations? responsible generative ai for a sustainable future

Reference 23

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Source-reported events for the cited work

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Observation 3240927a-a649-442b-a6fe-53bf37159b88 · outbound

This paper cites Ridgeway.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Ridgeway

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.579501Z

Source-reported events for the cited work

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Observation 4ab7a8fd-7307-4943-8a10-00abbb142260 · outbound

This paper cites Beyond content: How grammatical gender shapes visual representation in text-to-image models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Beyond content: How grammatical gender shapes visual representation in text-to-image models

Reference 25

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Source-reported events for the cited work

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Observation 21588d13-c0e5-4636-ae5c-70089027348a · outbound

This paper cites Fairt2i: Mitigating social bias in text-to-image generation via large language model-assisted detection and attribute rebalancing.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Fairt2i: Mitigating social bias in text-to-image generation via large language model-assisted detection and attribute rebalancing

Reference 26

Resolution: verified exact; raw_fallback, observed 2026-08-05T13:11:57.241483Z

Source-reported events for the cited work

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Observation 28f79eb1-7a1b-44a9-9d3f-fb158ba8bf86 · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 27

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Source-reported events for the cited work

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Observation ba96c041-587e-491f-9442-7c5ec5b81594 · outbound

This paper cites The Bias Amplification Paradox in Text-to-Image Generation.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations The Bias Amplification Paradox in Text-to-Image Generation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T13:11:56.907024Z

Source-reported events for the cited work

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Observation d8366cb4-ccbd-4a37-aecb-4f5fea61759f · outbound

This paper cites Representation bias in data: A survey on identification and resolution techniques.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Representation bias in data: A survey on identification and resolution techniques

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.561334Z

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Observation 6589023d-8f4c-4b06-b0ef-6a2e55e7ce19 · outbound

This paper cites Stable diffusion xl turbo.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Stable diffusion xl turbo

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.545748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed1e83cd-21fb-4e9f-9c41-923fa0dd6080 · outbound

This paper cites Smiling women pitching down: auditing representational and presentational gender biases in image-generative ai.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Smiling women pitching down: auditing representational and presentational gender biases in image-generative ai

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.528904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d637823f-1ae7-4fb7-b12f-d712a18088a7 · outbound

This paper cites Assessing social and intersectional biases in contextualized word representations.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Assessing social and intersectional biases in contextualized word representations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.512675Z

Source-reported events for the cited work

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Observation 8e26a813-73f1-4183-a13f-b0f13c376674 · outbound

This paper cites Quantifying Bias in Text-to-Image Generative Models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Quantifying Bias in Text-to-Image Generative Models

Reference 33

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T13:11:57.113670Z

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Observation 7912d891-6898-445c-b3a4-face43629507 · outbound

This paper cites New Job, New Gender? Measuring the Social Bias in Image Generation Models.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations New Job, New Gender? Measuring the Social Bias in Image Generation Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T13:11:56.934453Z

Source-reported events for the cited work

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Observation 9a5cde8b-79db-4258-8f7b-fd209d92b49f · outbound

This paper cites Evaluating fairness in large vision-language models across diverse demographic attributes and prompts.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations Evaluating fairness in large vision-language models across diverse demographic attributes and prompts

Reference 35

Resolution: verified exact; raw_fallback, observed 2026-08-05T13:11:57.072086Z

Source-reported events for the cited work

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Observation 76592f5c-d29a-4ddc-a53d-ae5524a8286f · outbound

This paper cites An athlete running in a stadium.

Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupations An athlete running in a stadium

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:11:57.496928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.