Fairness (machine learning)

Fairness refers to the various attempts at correcting algorithmic bias. While definitions of fairness are always controversial, results may be considered fair if they are independent of given variable, especially those considered sensitive, such as the traits of individuals that should not correlate with the outcome. 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 to certain groups or individuals. An example would be the way social media sites deliver personalized news to consumers.


Fairness refers to the various attempts at correcting algorithmic bias. While definitions of fairness are always controversial, results may be considered fair if they are independent of given variable, especially those considered sensitive, such as the traits of individuals that should not correlate with the outcome. 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 to certain groups or individuals. An example would be the way social media sites deliver personalized news to consumers.
Read article on Wikipedia