Multi-Objective Few-shot Learning for Fair Classification

TL;DR

This paper introduces a multi-objective learning framework that reduces disparities in classification outcomes related to secondary attributes like race or gender, without requiring extensive attribute annotations, thereby mitigating biases in predictive models.

Contribution

It presents a novel clustering-based heuristic integrated into a multi-objective learning framework to mitigate bias in classification tasks, especially in few-shot and zero-shot scenarios.

Findings

Effective bias mitigation on benchmark datasets

Works without secondary attribute annotations in zero-shot case

Reduces disparities with minimal attribute supervision

Abstract

In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e.g., race, gender etc.). Our proposed method involves learning a multi-objective function that in addition to learning the primary objective of predicting the primary class labels from the data, also employs a clustering-based heuristic to minimize the disparities of the class label distribution with respect to the cluster memberships, with the assumption that each cluster should ideally map to a distinct combination of attribute values. Experiments demonstrate effective mitigation of cognitive biases on a benchmark dataset without the use of annotations of secondary attribute values (the zero-shot case) or with the use of a small number of attribute value annotations (the few-shot case).