Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks (2504.06470v1)

Abstract: Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep learning to construct fair and informative representations. By introducing a novel penalty term during fine-tuning, our method enforces conditional independence between sensitive attributes and learned representations, addressing bias at its source while preserving predictive performance. Unlike prior methods, it supports diverse sensitive attributes, including continuous, discrete, binary, or multi-group types. Experiments on various types of data structure show that our approach achieves a superior balance between fairness and utility, significantly outperforming state-of-the-art baselines.

Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks

The paper, "Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks," addresses the increasingly critical challenge of ensuring fairness in ML models, particularly in contexts where biased data representations can lead to unjust predictions. The authors propose a method referred to as Deep Fair Learning (DFL), which integrates nonlinear sufficient dimension reduction with deep learning. This method aims to construct representations that are fair and informative by enforcing conditional independence between sensitive attributes and learned representations.

Key Contributions

1. Unified Fine-tuning Framework: The authors introduce a framework that incorporates nonlinear sufficient dimension reduction (SDR) to specifically address bias at the representational level. Unlike existing methods that attempt to adjust model outputs to adhere to certain fairness criteria, DFL aims to rectify bias within the data representations themselves. This distinction enables DFL to handle various sensitive attributes, whether continuous, discrete, or binary.
2. Formulation of Fairness: The core of the method is a fairness-promoting penalty included in the loss function, which drives the learned representations towards conditional independence from sensitive attributes. This feature highlights the framework's high-level perspective on fairness, aiming to address the intrinsic bias embedded within the data.
3. Experimental Validation: Through extensive experimentation on diverse datasets, the efficacy of DFL is demonstrated. The approach maintains predictive performance while significantly reducing both global disparities, such as True Positive Rate (TPR) gaps, and local disparities, like the Maximal Cumulative Disparity (MCDP). Crucially, these results surpass state-of-the-art baselines across various evaluated contexts.

Methodological Insights

DFL is grounded in the statistical concept of Sufficient Dimension Reduction (SDR), which maintains relevant information necessary for specific predictive tasks while excluding sensitive attributes. By minimizing the dependence between sensitive attributes and representation learning through nonlinear transformations, DFL proves theoretically robust and flexible.

The authors use distance covariance (DC) as a measure of dependence, offering a non-negative metric to enforce conditional independence. This choice underpins the theoretical foundation of the DFL framework, justifying the departure from more traditional fairness constraints.

Implications

Practically, DFL demonstrates its robustness across diverse tasks — ranging from tabular data to natural language processing and computer vision datasets. This suggests a potential for broad applicability in real-world scenarios.

Theoretically, DFL contributes by refocusing fairness objectives at the representational level, which may foster advancements in machine learning models used in high-stakes decision-making environments. By supporting a generalized framework for fairness, DFL facilitates the development of more adaptable models, capable of serving multiple downstream tasks without compromising fairness.

Future Directions

The research suggests multiple avenues for further exploration, which include optimizing computational efficiency for large datasets, integrating with generative models, and exploring the alignment with nuanced fairness metrics that might better cater to specific contexts.

In conclusion, the paper presents a compelling approach to machine learning fairness, eschewing superficial fairness optimization for a more foundational approach by addressing bias in data representations. The combination of theoretical rigor and practical effectiveness positions Deep Fair Learning as a noteworthy advancement in the field of fair machine learning.