Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation

TL;DR

This paper presents a framework for generating synthetic biased data to analyze the impact of biases on machine learning fairness and performance, combining empirical experiments with philosophical insights.

Contribution

It introduces a novel synthetic data generator for controlled bias analysis and explores the moral implications of biases in machine learning.

Findings

Biases significantly affect fairness metrics

Mitigation strategies can reduce bias impact

Synthetic data helps understand bias interactions

Abstract

Machine learning applications are becoming increasingly pervasive in our society. Since these decision-making systems rely on data-driven learning, risk is that they will systematically spread the bias embedded in data. In this paper, we propose to analyze biases by introducing a framework for generating synthetic data with specific types of bias and their combinations. We delve into the nature of these biases discussing their relationship to moral and justice frameworks. Finally, we exploit our proposed synthetic data generator to perform experiments on different scenarios, with various bias combinations. We thus analyze the impact of biases on performance and fairness metrics both in non-mitigated and mitigated machine learning models.