Understanding Bias in Machine Learning
This addresses the issue of bias in ML for domains like human resources and healthcare, but it is incremental as it focuses on visualization and explanation rather than new solutions.
The paper tackles the problem of bias in machine learning by explaining it from a technical perspective and illustrating its impact on models, using interactive plots with synthetic data to visualize learned bias.
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.