Reweighing
Explore the reweighing technique as a preprocessing method to mitigate bias in AI models by assigning greater weights to underrepresented samples. Understand how it balances minority and majority groups to ensure fair treatment across subpopulations. Learn the calculation of weights based on group and class frequencies and how to apply these weights in classifiers that support sample weighting.
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What is reweighing?
Reweighing is a preprocessing bias mitigation method. The main goal is to assign greater weights to underrepresented samples, which modifies the model to consider them more meaningful. Because the number of minority samples is lower, combining them with bigger weights balances the result, making it equally important for all subgroups.
Example
Let’s see how it works with an example. Imagine we have two possible classes (admitted, rejected) and three groups. Due to different population sizes, many models might prefer to learn ...