Introduction to Machine Learning with Python

Bias and Fairness in Ml

Author(s): Deepti Chopra and Roopal Khurana

Pp: 116-122 (7)

DOI: 10.2174/9789815124422123010012

* (Excluding Mailing and Handling)

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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