Abstract
In machine learning and AI, future predictions are based on past
observations, and bias is based on prior information. Harmful biases occur because of
human biases which are learned by an algorithm from the training data. In the previous
chapter, we discussed training versus testing, bounding the testing error, and VC
dimension. In this chapter, we will discuss bias and fairness.
Keywords: Bias, Confidence intervals, Fairness, Hypothesis testing.
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