Leveraging Multi-Task Learning to Transfer Demographic Fairness Across Tasks with Limited Demographic Information

Core Concepts

Multi-task learning can be used to transfer demographic fairness from a task with available demographic information to a related task without such information, enabling fairer models for the target task.

Abstract

The paper explores the use of multi-task learning (MTL) to transfer demographic fairness from one task to another, even when demographic information is only available for one of the tasks. The key insights are: MTL with a fairness loss on one task can produce fairer models for the other task, sometimes even outperforming models trained with fairness loss on the target task alone. This suggests that the MTL setup helps learn more generalizable and fair representations. The method can also enable intersectional fairness by leveraging single-axis demographic attributes from two different tasks. The fairness transfer is not dependent on domain or task similarity, but rather on the performance of the secondary task in the MTL setup. The authors evaluate their approach on various NLP datasets spanning clinical notes, online reviews, and social media, demonstrating the effectiveness of their MTL fairness transfer method.

Stats

Training models with a fairness loss can improve prediction fairness across different demographic groups. However, this requires demographic annotations for the training data, which are often unavailable. The authors propose using multi-task learning to transfer fairness from a task with demographic information to a related task without such information.

Quotes

"Drawing inspiration from transfer learning methods, we investigate whether we can utilize demographic data from a related task to improve the fairness of a target task." "We adapt a single-task fairness loss to a multi-task setting to exploit demographic labels from a related task in debiasing a target task, and demonstrate that demographic fairness objectives transfer fairness within a multi-task framework."

Key Insights Distilled From

Transferring Fairness using Multi-Task Learning with Limited Demographic Information

by Carlos Aguir... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2305.12671.pdf

Deeper Inquiries

How can the proposed MTL fairness transfer method be extended to handle more than two tasks with different demographic attributes?

To extend the MTL fairness transfer method to handle more than two tasks with different demographic attributes, a hierarchical multi-task learning approach can be implemented. In this setup, tasks can be organized in a hierarchical structure where tasks with similar demographic attributes are grouped together at different levels. The fairness loss can then be applied at each level of the hierarchy, allowing for the transfer of fairness across multiple tasks with varying demographic attributes. By incorporating a hierarchical structure, the method can effectively handle a larger number of tasks with diverse demographic attributes while ensuring fairness across the entire system.

What are the limitations of the method in terms of the required relationship between the tasks for successful fairness transfer?

One limitation of the method is the assumption of task similarity or relatedness for successful fairness transfer. The method relies on the underlying similarity between tasks to transfer fairness from one task to another. If the tasks are too dissimilar or unrelated, the transfer of fairness may not be effective. Additionally, the method requires access to demographic attributes for at least one task in the multi-task setup, which can be a limitation in scenarios where demographic data is scarce or unavailable. Furthermore, the method may face challenges in cases where the tasks have conflicting objectives or when the performance of the secondary task is significantly lower than the primary task.

Can the MTL fairness transfer approach be combined with other bias mitigation techniques, such as adversarial debiasing, to further improve the fairness of the models?

Yes, the MTL fairness transfer approach can be combined with other bias mitigation techniques, such as adversarial debiasing, to enhance the fairness of the models. Adversarial debiasing involves training an adversarial network to minimize the correlation between the model's predictions and the demographic attributes, thereby reducing bias in the model's output. By integrating adversarial debiasing with the MTL fairness transfer method, the models can benefit from both approaches simultaneously. This combined approach can provide a more comprehensive and robust solution to mitigating bias and promoting fairness in machine learning models.