Fairness (machine learning)

Fairness in <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> (ML) refers to the various attempts to correct <a href="/facts/Algorithmic_bias/vHaHsIdV">algorithmic bias</a> in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on <a href="/facts/Dependent_and_independent_variables/dINfzIF2">variables</a> considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
As is the case with many <a href="/facts/Ethics/wNVNgL5q">ethical</a> concepts, definitions of fairness and bias can be controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives.
Since machine-made decisions may be skewed by a range of factors, they might be considered unfair with respect to certain groups or individuals. An example could be the way <a href="/facts/Social_media/kUIkfpIA">social media</a> sites deliver personalized news to consumers.

Fairness (machine learning) open-in-new

Fairness (machine learning)