Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

TL;DR

This paper explores the complex interactions between fairness and accuracy in multi-task learning, highlighting the limitations of traditional methods and proposing new metrics and an approach to enhance fairness across multiple tasks.

Contribution

It introduces a novel set of metrics and a Multi-Task-Aware Fairness (MTA-F) approach to better balance fairness and accuracy in multi-task learning scenarios.

Findings

Traditional accuracy-focused methods may not optimize fairness effectively.

Proposed metrics better capture multi-dimensional fairness-accuracy trade-offs.

MTA-F approach improves fairness outcomes in real-world datasets.

Abstract

As multi-task models gain popularity in a wider range of machine learning applications, it is becoming increasingly important for practitioners to understand the fairness implications associated with those models. Most existing fairness literature focuses on learning a single task more fairly, while how ML fairness interacts with multiple tasks in the joint learning setting is largely under-explored. In this paper, we are concerned with how group fairness (e.g., equal opportunity, equalized odds) as an ML fairness concept plays out in the multi-task scenario. In multi-task learning, several tasks are learned jointly to exploit task correlations for a more efficient inductive transfer. This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks. We aim to provide a deeper understanding on how group fairness interacts with accuracy in multi-task learning, and we show that traditional approaches that mainly focus on optimizing the Pareto frontier of multi-task accuracy might not perform well on fairness goals. We propose a new set of metrics to better capture the multi-dimensional Pareto frontier of fairness-accuracy trade-offs uniquely presented in a multi-task learning setting. We further propose a Multi-Task-Aware Fairness (MTA-F) approach to improve fairness in multi-task learning. Experiments on several real-world datasets demonstrate the effectiveness of our proposed approach.