Identifying and Mitigating Bias in Machine Learning Applications
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Date
2019
Authors
Bald, Laura
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
This study addresses the existence of bias in machine learning applications and examines techniques for identifying and mitigating bias using scholarly literature published between 2012 and 2019. The intended audience is machine learning engineers, system analysts, and data analysts of any industry. This study is significant because there may be considerable ethical implications caused by machine learning bias; identifying and mitigating these biases is key to the development and deployment of effective machine learning algorithms.
Description
Keywords
Machine learning, Artificial intelligence, Bias, Variance, Machine bias, Ethics, Algorithmic bias, Machine learning models