4 Sources of Machine Learning Bias & How to Mitigate Impact

This guest post from Alegion explores the reality of machine learning bias and how to mitigate its impact on AI systems. It exists as a combination of algorithms and data; bias can occur in both of these elements. When we produce AI training data, we know to look for biases that can influence machine learning (ML). In our experience, there are four distinct types of bias that data scientists and AI developers should avoid vigilantly. The key to successfully mitigating bias is to first understand how and why it occurs.