Author: Megan Wells
Publisher: Scuba
Publication Year: N/A
Summary: The following article describes how with the rise of artificial intelligence (AI) and machine learning (ML), the use of data has multiplied over the years. The problem is that data is subject to biases that can alter the valuable purpose of a model built on the data. Although there are many biases, the most common biases in the field of data science are selection bias, demographic bias, measurement bias, confirmation bias, and association bias. Biased data can lead to false results or inaccurate predictive models that can affect a community of people. To avoid biasing data, one is to separate the signal from the noise, determine what’s essential, and ignore the noise. The second step is complying with privacy regulations by protecting customer information. Lastly, perform regular audits on your AI and ML algorithms. Unbiased data keeps model building accurate, and it limits an organization’s liabilities.