Fairness: Types of Bias  |  Machine Learning  |  Google for Developers (2024)

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Machine learning models are not inherently objective. Engineers train models byfeeding them a data set of training examples, and human involvement in the provisionand curation of this data can make a model's predictions susceptible to bias.

When building models, it's important to be aware of common human biases that canmanifest in your data, so you can take proactive steps to mitigate their effects.

Reporting Bias

Reporting bias occurs when the frequency of events, properties, and/or outcomescaptured in a data set does not accurately reflect their real-world frequency. This bias can arisebecause people tend to focus on documenting circ*mstances that are unusual or especially memorable,assuming that the ordinary can "go without saying."

Automation Bias

Automation bias is a tendency to favor results generated by automated systems over thosegenerated by non-automated systems, irrespective of the error rates of each.

Selection Bias

Selection bias occurs if a data set's examples are chosen in a waythat is not reflective of their real-world distribution. Selection bias can take many differentforms:

• Coverage bias: Data is not selected in a representative fashion.

• Non-response bias (or participation bias): Data ends up being unrepresentative due toparticipation gaps in the data-collection process.

• Sampling bias: Proper randomization is not used during data collection.

Group Attribution Bias

Group attribution bias is a tendency to generalize what is true of individuals to an entire group to whichthey belong. Two key manifestations of this bias are:

• In-group bias: A preference for members of a group to which you also belong, or for characteristicsthat you also share.

• Out-group hom*ogeneity bias: A tendency to stereotype individual members of a group to which you do notbelong, or to see their characteristics as more uniform.

Implicit Bias

Implicit bias occurs when assumptions are made based on one's own mental models and personal experiencesthat do not necessarily apply more generally.

A common form of implicit bias is confirmation bias, where model builders unconsciously process datain ways that affirm preexisting beliefs and hypotheses. In some cases, a model builder may actually keeptraining a model until it produces a result that aligns with their original hypothesis; this is calledexperimenter's bias.