Reducing bias in models
As we have discussed, the bias in a model is about certain attributes of a particular algorithm that cause it to create unfair outcomes. In the current world, there are known, well-documented general biases based on gender, race, and sexual orientation. It means that the data we collect is expected to exhibit those biases unless we are dealing with an environment where an effort has been made to remove them before collecting the data.
Most of the time, bias in algorithms is directly or indirectly introduced by humans. Humans introduce bias either unintentionally through negligence or intentionally through subjectivity. One of the reasons for human bias is the fact that the human brain is vulnerable to cognitive bias, which reflects a person’s own subjectivity, beliefs, and ideology in both the data process and logic creation process of an algorithm. Human bias can be reflected either in data used by the algorithm or in the formulation of the algorithm...
