Multi-Group Proportional Representation for Text-to-Image Models | Paper Citation Record

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This paper cites https://population.un.org/ wpp/.

Multi-Group Proportional Representation for Text-to-Image Models https://population.un.org/ wpp/

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This paper cites cdc.gov/SP.

Multi-Group Proportional Representation for Text-to-Image Models cdc.gov/SP

Reference 2

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This paper cites https://ec.europa.eu/eurostat/ web / population - demography / demography - population - stock - balance / database.

Multi-Group Proportional Representation for Text-to-Image Models https://ec.europa.eu/eurostat/ web / population - demography / demography - population - stock - balance / database

Reference 3

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Multi-Group Proportional Representation for Text-to-Image Models https:// ilostat.ilo.org/

Reference 4

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Multi-Group Proportional Representation for Text-to-Image Models https : / / www

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This paper cites employed persons by detailed oc- cupation, sex, race, and hispanic or latino ethnicity.

Multi-Group Proportional Representation for Text-to-Image Models employed persons by detailed oc- cupation, sex, race, and hispanic or latino ethnicity

Reference 6

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Multi-Group Proportional Representation for Text-to-Image Models https://data.worldbank

Reference 7

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This paper cites Firefly, 2024.

Multi-Group Proportional Representation for Text-to-Image Models Firefly, 2024

Reference 8

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This paper cites How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?.

Multi-Group Proportional Representation for Text-to-Image Models How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?

Reference 9

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This paper cites Pioneer programmer: Jean Jennings Bartik and the computer that changed the world.

Multi-Group Proportional Representation for Text-to-Image Models Pioneer programmer: Jean Jennings Bartik and the computer that changed the world

Reference 10

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This paper cites Easily acces- sible text-to-image generation amplifies demographic stereo- types at large scale.

Multi-Group Proportional Representation for Text-to-Image Models Easily acces- sible text-to-image generation amplifies demographic stereo- types at large scale

Reference 11

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This paper cites Inte- grating structured biological data by kernel maximum mean discrepancy.

Multi-Group Proportional Representation for Text-to-Image Models Inte- grating structured biological data by kernel maximum mean discrepancy

Reference 12

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This paper cites Con- centration inequalities: A nonasymptotic theory of indepen- dence.

Multi-Group Proportional Representation for Text-to-Image Models Con- centration inequalities: A nonasymptotic theory of indepen- dence

Reference 13

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This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

Multi-Group Proportional Representation for Text-to-Image Models PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 14

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

Multi-Group Proportional Representation for Text-to-Image Models Tibet: Identifying and evaluating biases in text-to-image generative models

Reference 15

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This paper cites Dall-eval: Prob- ing the reasoning skills and social biases of text-to-image generation models.

Multi-Group Proportional Representation for Text-to-Image Models Dall-eval: Prob- ing the reasoning skills and social biases of text-to-image generation models

Reference 16

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Observation 890a5524-d8c1-426f-9e3b-559fb29162d9 · outbound

This paper cites Fair sampling in diffusion models through switching mechanism.

Multi-Group Proportional Representation for Text-to-Image Models Fair sampling in diffusion models through switching mechanism

Reference 17

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Observation 9c684aa0-0217-49f3-ac9d-fc613a848f56 · outbound

This paper cites Debiasing Vision-Language Models via Biased Prompts.

Multi-Group Proportional Representation for Text-to-Image Models Debiasing Vision-Language Models via Biased Prompts

Reference 18

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This paper cites Openbias: Open-set bias detection in text-to-image generative models.

Multi-Group Proportional Representation for Text-to-Image Models Openbias: Open-set bias detection in text-to-image generative models

Reference 19

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This paper cites Openbias: Open-set bias detection in text-to-image generative models.

Multi-Group Proportional Representation for Text-to-Image Models Openbias: Open-set bias detection in text-to-image generative models

Reference 20

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This paper cites Diversity is not a one-way street: Pilot study on ethical inter- ventions for racial bias in text-to-image systems.

Multi-Group Proportional Representation for Text-to-Image Models Diversity is not a one-way street: Pilot study on ethical inter- ventions for racial bias in text-to-image systems

Reference 21

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This paper cites A friendly face: Do text-to-image systems rely on stereotypes when the input is under-specified? In The AAAI-23 Workshop on Creative AI Across Modalities, 2023.

Multi-Group Proportional Representation for Text-to-Image Models A friendly face: Do text-to-image systems rely on stereotypes when the input is under-specified? In The AAAI-23 Workshop on Creative AI Across Modalities, 2023

Reference 22

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This paper cites Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness.

Multi-Group Proportional Representation for Text-to-Image Models Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness

Reference 23

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This paper cites Unified concept editing in diffusion models.

Multi-Group Proportional Representation for Text-to-Image Models Unified concept editing in diffusion models

Reference 24

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This paper cites Introducing gemini: our largest and most capable ai model, 2023.

Multi-Group Proportional Representation for Text-to-Image Models Introducing gemini: our largest and most capable ai model, 2023

Reference 25

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This paper cites A kernel two-sample test.

Multi-Group Proportional Representation for Text-to-Image Models A kernel two-sample test

Reference 26

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This paper cites Gender recognition or gender reductionism? the social implications of embedded gender recognition systems.

Multi-Group Proportional Representation for Text-to-Image Models Gender recognition or gender reductionism? the social implications of embedded gender recognition systems

Reference 27

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This paper cites Multicalibration: Calibration for the (computationally-identifiable) masses.

Multi-Group Proportional Representation for Text-to-Image Models Multicalibration: Calibration for the (computationally-identifiable) masses

Reference 28

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This paper cites Lora: Low-rank adaptation of large language models.

Multi-Group Proportional Representation for Text-to-Image Models Lora: Low-rank adaptation of large language models

Reference 29

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

Multi-Group Proportional Representation for Text-to-Image Models Fairface: Face at- tribute dataset for balanced race, gender, and age for bias measurement and mitigation

Reference 30

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This paper cites De-stereotyping text-to-image models through prompt tuning.

Multi-Group Proportional Representation for Text-to-Image Models De-stereotyping text-to-image models through prompt tuning

Reference 31

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Multi-Group Proportional Representation for Text-to-Image Models Multi- accuracy: Black-box post-processing for fairness in classifi- cation

Reference 32

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This paper cites Training unbiased diffusion models from biased dataset.

Multi-Group Proportional Representation for Text-to-Image Models Training unbiased diffusion models from biased dataset

Reference 33

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Multi-Group Proportional Representation for Text-to-Image Models Unresolved cited work

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This paper cites Proving ground: The untold story of the six women who programmed the world’s first modern computer.

Multi-Group Proportional Representation for Text-to-Image Models Proving ground: The untold story of the six women who programmed the world’s first modern computer

Reference 35

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This paper cites Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation.

Multi-Group Proportional Representation for Text-to-Image Models Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation

Reference 36

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

Multi-Group Proportional Representation for Text-to-Image Models Fair Text-to-Image Diffusion via Fair Mapping

Reference 37

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This paper cites Controlling the fidelity and diversity of deep generative models via pseudo density.

Multi-Group Proportional Representation for Text-to-Image Models Controlling the fidelity and diversity of deep generative models via pseudo density

Reference 38

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This paper cites Stable bias: Evaluating societal representa- tions in diffusion models.

Multi-Group Proportional Representation for Text-to-Image Models Stable bias: Evaluating societal representa- tions in diffusion models

Reference 39

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This paper cites BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models.

Multi-Group Proportional Representation for Text-to-Image Models BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models

Reference 40

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This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Multi-Group Proportional Representation for Text-to-Image Models Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 41

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This paper cites they only care to show us the wheelchair.

Multi-Group Proportional Representation for Text-to-Image Models they only care to show us the wheelchair

Reference 42

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This paper cites On the method of bounded differences.

Multi-Group Proportional Representation for Text-to-Image Models On the method of bounded differences

Reference 43

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This paper cites Training diffusion models towards diverse image generation with reinforcement learning.

Multi-Group Proportional Representation for Text-to-Image Models Training diffusion models towards diverse image generation with reinforcement learning

Reference 44

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This paper cites Midjourney.com, 2024.

Multi-Group Proportional Representation for Text-to-Image Models Midjourney.com, 2024

Reference 45

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This paper cites On the epistemic limits of personalized prediction.

Multi-Group Proportional Representation for Text-to-Image Models On the epistemic limits of personalized prediction

Reference 46

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This paper cites Integral probability metrics and their generat- ing classes of functions.

Multi-Group Proportional Representation for Text-to-Image Models Integral probability metrics and their generat- ing classes of functions

Reference 47

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This paper cites Multi-group proportional representation in retrieval.

Multi-Group Proportional Representation for Text-to-Image Models Multi-group proportional representation in retrieval

Reference 48

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This paper cites Editing implicit assumptions in text-to-image diffusion models.

Multi-Group Proportional Representation for Text-to-Image Models Editing implicit assumptions in text-to-image diffusion models

Reference 49

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This paper cites Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness.

Multi-Group Proportional Representation for Text-to-Image Models Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness

Reference 50

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This paper cites Bal- ancing act: Distribution-guided debiasing in diffusion models.

Multi-Group Proportional Representation for Text-to-Image Models Bal- ancing act: Distribution-guided debiasing in diffusion models

Reference 51

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Observation aaa03dd1-fccb-4583-85a5-d1ed050a5cd9 · outbound

This paper cites Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models.

Multi-Group Proportional Representation for Text-to-Image Models Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Reference 52

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This paper cites Computational optimal transport: With applications to data science.

Multi-Group Proportional Representation for Text-to-Image Models Computational optimal transport: With applications to data science

Reference 53

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Observation 9b9c12ec-7f57-4c56-8ce0-fe8135e6232d · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.

Multi-Group Proportional Representation for Text-to-Image Models Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 54

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This paper cites Gemini image generation got it wrong.

Multi-Group Proportional Representation for Text-to-Image Models Gemini image generation got it wrong

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:40.321425Z

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This paper cites Zero-shot text-to-image generation.

Multi-Group Proportional Representation for Text-to-Image Models Zero-shot text-to-image generation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:40.134297Z

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This paper cites Google apologizes for ‘missing the mark’ after gemini generated racially diverse nazis.

Multi-Group Proportional Representation for Text-to-Image Models Google apologizes for ‘missing the mark’ after gemini generated racially diverse nazis

Reference 57

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This paper cites High-resolution image synthesis with latent diffusion models.

Multi-Group Proportional Representation for Text-to-Image Models High-resolution image synthesis with latent diffusion models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.788904Z

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This paper cites Finetuning Text-to-Image Diffusion Models for Fairness.

Multi-Group Proportional Representation for Text-to-Image Models Finetuning Text-to-Image Diffusion Models for Fairness

Reference 59

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This paper cites Generalized people diversity: Learning a human perception-aligned diversity representa- tion for people images.

Multi-Group Proportional Representation for Text-to-Image Models Generalized people diversity: Learning a human perception-aligned diversity representa- tion for people images

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.656446Z

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This paper cites On the empirical estimation of integral probability metrics.

Multi-Group Proportional Representation for Text-to-Image Models On the empirical estimation of integral probability metrics

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.440303Z

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This paper cites Stable diffusion v2.1 and dream- studio updates 7-dec 22, 2022.

Multi-Group Proportional Representation for Text-to-Image Models Stable diffusion v2.1 and dream- studio updates 7-dec 22, 2022

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.259979Z

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Observation 6ebb42bf-9017-468f-83d3-399a22b4a4a2 · outbound

This paper cites Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation.

Multi-Group Proportional Representation for Text-to-Image Models Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation

Reference 63

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This paper cites Concept algebra for (score-based) text-controlled generative models.

Multi-Group Proportional Representation for Text-to-Image Models Concept algebra for (score-based) text-controlled generative models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.126565Z

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This paper cites MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models.

Multi-Group Proportional Representation for Text-to-Image Models MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models

Reference 65

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This paper cites Iti- gen: Inclusive text-to-image generation.

Multi-Group Proportional Representation for Text-to-Image Models Iti- gen: Inclusive text-to-image generation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:39.011540Z

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Observation 59ab6bc3-6821-4e6b-ac15-6eafff779773 · outbound

This paper cites a photo of concept.

Multi-Group Proportional Representation for Text-to-Image Models a photo of concept

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:38.874335Z

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Observation 4c9a4251-f005-41a1-84bf-3ad4070d87f9 · outbound

This paper cites Male”, “Old.

Multi-Group Proportional Representation for Text-to-Image Models Male”, “Old

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:38.689115Z

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