What is machine learning bias (algorithm bias or AI bias)? - Definition from WhatIs.com | TechTarget

Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process, as TechTarget reports.
 

Algorithms can have built-in biases because they are created by individuals who have conscious or unconscious preferences that may go undiscovered until the algorithms are used, and potentially amplified, publically.

High bias is a reflection of problems related to the gathering or usage of data, where systems draw improper conclusions about data sets. This is often due to human intervention or the researchers' lack of cognitive assessment. Types of cognitive bias that can be inadvertently applied to algorithms are stereotyping, bandwagon effects, confirmation bias, priming and selective perception.

Machine learning, a subset of artificial intelligence, depends on the quality, objectivity and size of learning data sets. Since machine-learning algorithms and pattern recognition abilities operate in the world defined by the data used to calibrate them, a lack of truly random or complete data can conclude in bias.
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Source: TechTarget