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.
About this chapter
Cite this chapter as:
Deepti Chopra, Roopal Khurana ;Bias and Fairness in Ml, Introduction to Machine Learning with Python (2023) 1: 116. https://doi.org/10.2174/9789815124422123010012
DOI https://doi.org/10.2174/9789815124422123010012 |
Publisher Name Bentham Science Publisher |