| dbo:abstract |
Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on variables considered sensitive. Examples of these kinds of variable include gender, ethnicity, sexual orientation, disability and more. As it is the case with many ethical concepts, definitions of fairness and bias are always controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives. In machine learning, the problem of algorithmic bias is well known and well studied. Outcomes may be skewed by a range of factors and thus might be considered unfair with respect to certain groups or individuals. An example would be the way social media sites deliver personalized news to consumers. (en) En aprendizaje automático, un algoritmo es justo, o tiene equidad si sus resultados son independientes de un cierto conjunto de variables que consideramos sensibles y no relacionadas con él (p.e.: género, etnia, orientación sexual, etc.). (es) |
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En aprendizaje automático, un algoritmo es justo, o tiene equidad si sus resultados son independientes de un cierto conjunto de variables que consideramos sensibles y no relacionadas con él (p.e.: género, etnia, orientación sexual, etc.). (es) Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on variables considered sensitive. Examples of these kinds of variable include gender, ethnicity, sexual orientation, disability and more. As it is the case with many ethical concepts, definitions of fairness and bias are always controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives. In machine learning, the problem of algorithmic bias is well known and well studied. Outcomes may be skewed by a range of factors and thus might be considered unfair with respect to certain (en) |
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Fairness (machine learning) (en) Equidad (aprendizaje automático) (es) |
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