Reweighting algorithm for fairness

Hello,

I want to apply reweighting to mitigate fairness. I saw there is one way in matlab but when I use it, It does not do anything to the method. The results / predictions are exactly the same. What I do is the following:

fairWeights = fairnessWeights(trainingData,attributename,"Eval");
predictors.Weights= fairWeights;
classificationSVM = fitcsvm(...
    predictors, ...
    response, ...
    'KernelFunction','rbf',...
    
'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',
opts,'Standardize',true,Weights=
"Weights");

and predictors contain the fair weights-

fitcsvm returns the same predictions without weights. How can I do reweighting in matlab then?. The example of matlab has a initial weight as a predictor so it does not make sense as in a real example you do not have weights computed as a features in the model.

Thank you

Answer 1

[Rohit]

Hi Esmeralda,

Since I do not have access to data and know which feature you are using to get new weights, predicting the exact reason for your issue is hard. But here are a few things you can try to troubleshoot the issue:

1. Verify that the “fairnessWeights” function is returning non-uniform weights. You can do this by printing out the values of fairWeights and checking that they are different from each other.
2. Ensure that the fairness issue you are trying to mitigate is present in the data. If the data is already fair, reweighting may not have any effect on the predictions.

Please refer to the following MathWorks documentation to understand and visualize fairness weights: https://www.mathworks.com/help/stats/fairnessweights.html