Author: Sean Michael Kerner
Publisher: VentureBeat
Publication Year: 2022
Summary: The following article discusses how when labeling data for modeling and machine learning, the person labeling the data can accidently introduce bias to the models. This article argues that an open-source way to model (i.e. let a group of people label the data) can help to reduce these biases. This is an interesting idea: while labeling things yourself is a good start, having more people label things can help to reduce bias.
