Enhancing Model Fairness and Accuracy with Similarity Networks: A Methodological Approach

TL;DR

This paper introduces a similarity network approach to identify bias sources in datasets, improving fairness and accuracy in machine learning tasks through adjustable similarity resolution and versatile applications.

Contribution

The paper presents a novel similarity network methodology that enhances understanding of dataset biases and promotes fairer, more accurate models across various tasks.

Findings

Effective bias exploration in datasets

Improved fairness in classification tasks

Versatile application in data imputation and augmentation

Abstract

In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we use different techniques to map instances into a similarity feature space. Our method's ability to adjust the resolution of pairwise similarity provides clear insights into the relationship between the dataset classification complexity and model fairness. Experimental results confirm the promising applicability of the similarity network in promoting fair models. Moreover, leveraging our methodology not only seems promising in providing a fair downstream task such as classification, it also performs well in imputation and augmentation of the dataset satisfying the fairness criteria such as demographic parity and imbalanced classes.