To prevent an algorithm from only favoring the majority, you can use several mathematical techniques. One common method is resampling. You can use oversampling to create more examples of the minority group or undersampling to reduce the number of majority examples. This helps the model see the smaller groups more clearly without losing important patterns.
Another effective approach is cost-sensitive learning. Instead of treating every mistake equally, you assign a higher penalty when the model misclassifies a minority group member. This forces the math to prioritize accuracy for everyone, not just the largest group.
You can also use reweighting. This involves assigning a higher mathematical weight to minority samples during the training process. This way, the algorithm treats each minority instance as more important than a majority instance. By using these methods, you can create a fairer model that identifies everyone correctly without sacrificing the total predictive power of your system. These techniques ensure that small groups are not ignored by the computer's logic.
