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Addressing Robust Fairness in Adversarial Learning through Distributional Optimization

Core Concepts
The core message of this paper is to address the robust fairness issue in adversarial learning by leveraging distributional robust optimization (DRO) to learn class-wise distributionally adversarial weights, which can enhance the model's robustness and fairness simultaneously.
The paper investigates the robust fairness issue in adversarial training, where the robust accuracy can vary significantly across different classes or categories. The authors propose a novel learning paradigm called Fairness-Aware Adversarial Learning (FAAL) to address this challenge. The key highlights are: The authors analyze the robust fairness issue from the perspective of group/class distributional shift, and propose to leverage distributional robust optimization (DRO) to handle this challenge. FAAL extends the conventional min-max adversarial training framework into a min-max-max formulation, where the intermediate maximization step is dedicated to learning the class-wise distributionally adversarial weights. Comprehensive experiments on CIFAR-10 and CIFAR-100 datasets demonstrate that FAAL can fine-tune a robust but unfair model into a model with both fairness and robustness within only two epochs, without compromising the overall clean and robust accuracies. The authors also show that combining FAAL with stronger adversarial attacks (e.g., AWP) can further boost the worst-class robust accuracy without sacrificing the average performance. When training the model from scratch, FAAL also outperforms state-of-the-art methods like CFA and WAT in terms of achieving high worst-class robust accuracy while maintaining comparable average robustness.
The robust accuracy on the "cat" class is the lowest among all classes in the adversarially trained Wide-ResNet34-10 model on CIFAR-10. The most significant disparity between clean and robust accuracy arises in the "deer" class.
"The robust fairness issue in the conventional AT is due to the unknown group (class) distribution shift induced by the generated adversarial perturbations, which results in the overfitting problem." "Rather than assuming a fixed uniform data distribution, DRO acknowledges the inherent distributional uncertainty in real-world data, offering a more resilient and adaptable model structure."

Key Insights Distilled From

by Yanghao Zhan... at 03-29-2024
Towards Fairness-Aware Adversarial Learning

Deeper Inquiries

How can the proposed FAAL framework be extended to handle other types of distribution shifts beyond the class-wise shifts considered in this paper

The FAAL framework can be extended to handle other types of distribution shifts by modifying the uncertainty set Q in the distributional robust optimization (DRO) formulation. Instead of focusing solely on class-wise shifts, the uncertainty set can be defined to encompass shifts in other data characteristics or features. For example, in natural language processing tasks, the uncertainty set could be defined to capture shifts in word distributions, syntactic structures, or semantic meanings. By adapting the DRO framework to consider these different types of distribution shifts, the FAAL approach can be applied to a wider range of machine learning tasks beyond class-wise shifts in computer vision.

What are the potential limitations of the DRO-based approach in FAAL, and how can they be addressed in future research

One potential limitation of the DRO-based approach in FAAL is the computational complexity and scalability of solving the optimization problem for each batch of data. As the number of classes or features increases, the optimization problem becomes more complex and time-consuming to solve. To address this limitation, future research could explore approximation techniques or optimization algorithms that can efficiently handle large-scale DRO problems. Additionally, incorporating parallel computing or distributed computing strategies could help improve the scalability of the FAAL framework for handling larger and more complex datasets.

Can the insights from this work on robust fairness be applied to other machine learning tasks beyond computer vision, such as natural language processing or reinforcement learning

The insights from this work on robust fairness can be applied to other machine learning tasks beyond computer vision, such as natural language processing (NLP) or reinforcement learning. In NLP tasks, the concept of robust fairness can be translated to ensuring consistent and equitable model performance across different language domains, dialects, or text genres. This can help mitigate biases and disparities in NLP models when processing diverse linguistic data. In reinforcement learning, robust fairness can be applied to ensure that reinforcement learning agents exhibit fair and consistent behavior across different environments or scenarios, preventing biases or unfair advantages in decision-making processes. By adapting the principles of robust fairness from this work, machine learning models in various domains can be enhanced to be more reliable, equitable, and robust.