spostrzeżenie - Artificial Intelligence - # Fairness in Text-to-Image Models

Addressing Biases in Text-to-Image Diffusion Models for Fairness

Q: How can the proposed method be adapted to address biases related to non-binary identities?

The proposed method can be adapted to address biases related to non-binary identities by expanding the classification system used for distributional alignment. Instead of solely focusing on binary categories like male and female, additional categories representing non-binary identities can be incorporated into the classifier. This would involve training the classifiers on a diverse dataset that includes images and labels representing non-binary individuals. By including these additional categories in the target distribution for alignment, the model can learn to generate images that better represent and include individuals with non-binary gender identities.

Q: What are the potential limitations or unintended consequences of using classifiers trained on face images to define protected groups?

Using classifiers trained on face images to define protected groups may have several limitations and unintended consequences. One limitation is that facial features alone may not fully capture an individual's identity or group membership, leading to oversimplification and potential misclassification. This could result in reinforcing stereotypes or biases based on physical appearance rather than accurately representing diverse identities within each group. Additionally, relying solely on facial features for classification may overlook intersectionality - where individuals belong to multiple marginalized groups simultaneously (e.g., being both Black and female). This could lead to underrepresentation or misrepresentation of certain intersectional identities in generated images. Moreover, there is a risk of perpetuating existing biases present in the training data used for classifier training. If the training data itself contains biased representations or lacks diversity, this bias will be reflected in how protected groups are defined by the classifier. As a result, any bias present in defining these groups will carry over into image generation tasks.

Q: How might cultural biases be addressed in addition to biases centered around human representation?

Addressing cultural biases alongside biases centered around human representation requires a multi-faceted approach: Diverse Training Data: Ensuring that datasets used for training models encompass a wide range of cultures, traditions, and perspectives is crucial. Including diverse cultural references helps prevent models from favoring one culture over another when generating content. Cultural Sensitivity Guidelines: Implementing guidelines during model development that promote cultural sensitivity can help mitigate unintentional reinforcement of stereotypes or offensive depictions. Human-in-the-Loop Validation: Incorporating human evaluators from various cultural backgrounds during model validation stages can provide valuable feedback on potentially biased outputs. Intersectionality Consideration: Recognizing intersecting factors such as race, gender identity, age etc., ensures more nuanced representations across different dimensions of diversity. Regular Audits & Bias Checks: Periodic audits should be conducted post-deployment to identify any emerging cultural biases and take corrective actions promptly. By integrating these strategies into model development processes and actively addressing issues related to cultural bias alongside human representation bias, AI systems can strive towards more inclusive and culturally sensitive outcomes in their generated content.

Główne pojęcia

Fairness in text-to-image diffusion models is achieved through distributional alignment and adjusted direct finetuning, reducing biases while maintaining image quality.

Streszczenie

The rapid adoption of text-to-image diffusion models has raised concerns about biases. This work proposes a method to address biases by aligning specific attributes of generated images with target distributions. The approach includes a distributional alignment loss and adjusted direct finetuning of the sampling process. Empirical results show significant reductions in gender, racial, and intersectional biases for occupational prompts. The method supports diverse perspectives of fairness beyond absolute equality. It is scalable and can debias multiple concepts simultaneously.

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arxiv.org

Statystyki

Gender bias is significantly reduced even when finetuning just five soft tokens.
Our method markedly reduces gender, racial, and their intersectional biases for occupational prompts.
Age distribution aligned to 75% young and 25% old ratio while debiasing gender and race.

Cytaty

"Our method markedly reduces gender, racial, and their intersectional biases for occupational prompts."
"Gender bias is significantly reduced even when finetuning just five soft tokens."

Kluczowe wnioski z

Finetuning Text-to-Image Diffusion Models for Fairness

by Xudong Shen,... o arxiv.org 03-18-2024

https://arxiv.org/pdf/2311.07604.pdf

Finetuning Text-to-Image Diffusion Models for Fairness

Głębsze pytania

How can the proposed method be adapted to address biases related to non-binary identities?

The proposed method can be adapted to address biases related to non-binary identities by expanding the classification system used for distributional alignment. Instead of solely focusing on binary categories like male and female, additional categories representing non-binary identities can be incorporated into the classifier. This would involve training the classifiers on a diverse dataset that includes images and labels representing non-binary individuals. By including these additional categories in the target distribution for alignment, the model can learn to generate images that better represent and include individuals with non-binary gender identities.

What are the potential limitations or unintended consequences of using classifiers trained on face images to define protected groups?

Using classifiers trained on face images to define protected groups may have several limitations and unintended consequences. One limitation is that facial features alone may not fully capture an individual's identity or group membership, leading to oversimplification and potential misclassification. This could result in reinforcing stereotypes or biases based on physical appearance rather than accurately representing diverse identities within each group.
Additionally, relying solely on facial features for classification may overlook intersectionality - where individuals belong to multiple marginalized groups simultaneously (e.g., being both Black and female). This could lead to underrepresentation or misrepresentation of certain intersectional identities in generated images.
Moreover, there is a risk of perpetuating existing biases present in the training data used for classifier training. If the training data itself contains biased representations or lacks diversity, this bias will be reflected in how protected groups are defined by the classifier. As a result, any bias present in defining these groups will carry over into image generation tasks.

How might cultural biases be addressed in addition to biases centered around human representation?

Addressing cultural biases alongside biases centered around human representation requires a multi-faceted approach:

Diverse Training Data: Ensuring that datasets used for training models encompass a wide range of cultures, traditions, and perspectives is crucial. Including diverse cultural references helps prevent models from favoring one culture over another when generating content.

Cultural Sensitivity Guidelines: Implementing guidelines during model development that promote cultural sensitivity can help mitigate unintentional reinforcement of stereotypes or offensive depictions.

Human-in-the-Loop Validation: Incorporating human evaluators from various cultural backgrounds during model validation stages can provide valuable feedback on potentially biased outputs.

Intersectionality Consideration: Recognizing intersecting factors such as race, gender identity, age etc., ensures more nuanced representations across different dimensions of diversity.

Regular Audits & Bias Checks: Periodic audits should be conducted post-deployment to identify any emerging cultural biases and take corrective actions promptly.

By integrating these strategies into model development processes and actively addressing issues related to cultural bias alongside human representation bias, AI systems can strive towards more inclusive and culturally sensitive outcomes in their generated content.