toplogo
Sign In

Enforcing Conditional Independence in Latent Representations for Fair and Causal Image Generation


Core Concepts
The authors propose a novel approach to enforce conditional independence in the latent representations of machine learning models, enabling fair and causal image generation.
Abstract
The paper introduces a framework to ensure fair and unconfounded representation learning during training. The key contributions are: The authors extend the theoretical idea of expressing the conditional independence constraint as an equality of two Jensen-Shannon divergences to high dimensional latent spaces using a dynamic sampling technique. This can be applied to any encoder architecture. They demonstrate the effectiveness of enforcing conditional independence on the latent representation (v-space) compared to enforcing it on the output label (y-space). The v-space approach achieves higher accuracy, fairness, and disentanglement of sensitive attributes in the latent representation. The authors apply their framework to the diffusion autoencoder model, enabling causal image generation with controllable latent spaces. By enforcing conditional independence on only a portion of the semantic subcode, the model can effectively disentangle sensitive attributes like skin type while preserving the ability to generate realistic images. The experiments on synthetic data and the Gender Shades face image dataset show that the proposed v-space conditional independence enforcement outperforms various baselines in terms of accuracy, fairness, and disentanglement of sensitive attributes.
Stats
The theoretical maximum accuracy under conditional independence for the synthetic dataset is 0.83. The vanilla diffusion autoencoder achieves 93.0% balanced accuracy on the Gender Shades dataset. The v-space CI-DiffAE model achieves 96.6% balanced accuracy on the Gender Shades dataset, with an equality of opportunity (EO) gap of 5.0%.
Quotes
"Enforcing conditional independence with respect to only the label is limited in its ability to enforce the equalized odds fairness constraint effectively." "By enforcing conditional independence with respect to only a portion of the semantic subcode, we produce a latent representation that is invariant to a protected attribute of choice."

Deeper Inquiries

How can the proposed framework be extended to handle multiple sensitive attributes simultaneously in the latent representation

The proposed framework can be extended to handle multiple sensitive attributes simultaneously in the latent representation by modifying the dynamic sampling procedure and the conditional independence enforcement mechanism. One approach could involve partitioning the latent space into subspaces, each corresponding to a different sensitive attribute. By enforcing conditional independence separately for each subspace with respect to its corresponding attribute, the model can learn to disentangle multiple sensitive attributes. Additionally, the dynamic sampling procedure can be adapted to sample from the joint distribution of all sensitive attributes, ensuring that the learned representations are invariant to all attributes simultaneously. This extension would require careful design of the loss function, discriminators, and sampling strategy to effectively capture the interactions between multiple attributes in the latent space.

What other applications beyond image generation could benefit from the ability to enforce conditional independence in high-dimensional latent spaces

Beyond image generation, the ability to enforce conditional independence in high-dimensional latent spaces has various applications in machine learning and artificial intelligence. One potential application is in natural language processing, where fair representation learning is crucial for mitigating biases in language models. By enforcing conditional independence in the latent space of language models, researchers can ensure that the learned representations are not influenced by sensitive attributes such as gender, race, or ethnicity. This can lead to more equitable language models that produce unbiased and inclusive outputs across diverse demographic groups. Additionally, the framework could be applied to healthcare data analysis, financial modeling, and recommendation systems to address fairness and bias issues in decision-making processes.

How can the dynamic sampling procedure be further improved to provide stronger theoretical guarantees on the learned fair representations

To provide stronger theoretical guarantees on the learned fair representations, the dynamic sampling procedure can be further improved in several ways. One approach is to incorporate probabilistic sampling techniques that account for uncertainty in the latent space distributions. By sampling from probabilistic distributions rather than deterministic values, the model can capture the variability and complexity of the data more effectively. Additionally, leveraging techniques from causal inference and information theory can enhance the interpretability and robustness of the learned representations. By incorporating causal reasoning principles into the dynamic sampling procedure, the model can better disentangle causal factors from spurious correlations, leading to more reliable and trustworthy fair representations. Furthermore, exploring advanced sampling algorithms such as Markov Chain Monte Carlo (MCMC) methods or variational inference techniques can improve the efficiency and convergence of the dynamic sampling process, ensuring that the learned representations adhere to the desired fairness constraints.
0