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

• URL: http://arxiv.org/abs/2209.05889v1
• Date: Tue, 13 Sep 2022 11:18:50 GMT
• Title: Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation
• Authors: Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele Regoli, Andrea Cosentini
• Abstract summary: Machine learning applications are becoming increasingly pervasive in our society. Risk is that they will systematically spread the bias embedded in data. We propose to analyze biases by introducing a framework for generating synthetic data with specific types of bias and their combinations.
• Score: 66.64736150040093
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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