Understanding Bias in Machine Learning

Abstract

Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.

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Cited by in corpus (4)

• A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems
• Explaining medical AI performance disparities across sites with confounder Shapley value analysis
• Person Perception Biases Exposed: Revisiting the First Impressions Dataset
• Patterns Count-Based Labels for Datasets