Bias in Data-Informed Decision Making

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Author(s): Harini Dissanayake (DOT loves data, New Zealand)and Paul J. Bracewell (DOT loves data, New Zealand)
Copyright: 2023
Pages: 14
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch068

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

With vast amounts of data becoming available in a machine-readable format, decision makers in almost every sector are focused on exploiting data and machine learning to drive the phenomena of automated decision making, whilst rapidly dissolving the human oversight in the process. The description of bias in decision making arising from machine learning outlined in this article sets to demonstrate the scale of the issue and the value of transparency in decision making that affects the daily life of ordinary humans. Fully automated solutions without appropriate governance can be problematic due to the replication of human biases which can be captured and imposed upon the training process of a machine learning models. Decision makers must be aware of limitations within the data and proceed with caution. As such, transparency in how data is used to make decisions is vital. Despite the ever-increasing reliance on algorithms in decision making, human oversight is critical. This article serves to raise awareness of this aspect of machine learning.

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