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Machine bias is the effect of erroneous assumptions in machine learning processes. Bias reflects problems related to the gathering or use of data, where systems draw improper conclusions about data sets, either because of human intervention or as a result of a lack of cognitive assessment of data.
Machine bias is also known as algorithm bias or simply bias.
Machine bias takes various forms. One of the most prominent examples involves the use of machine learning systems to make judgments about individual people or groups of people. For example, when used in the field of criminal justice, some machine learning models have been shown to assume higher crime rates for individuals based on superficial data such as ethnicity or location.
Another way to explain machine bias in scientific terms is by describing it as a "clustering" of data that is not inherently justified, where bias is one part of what engineers talk about as a "bias-variance" trade-off. High bias can cause improper clustering. High variance can cause excessive data scattering. Engineers might refer to a system or result as "high bias, high variance" or "low bias, high variance" or some other combination.