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Trusted-AI / AIF360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
https://aif360.res.ibm.com/
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Learning Fair Representations Issue #253

Open MihirMishra23 opened 3 years ago

MihirMishra23 commented 3 years ago

The Learning Fair Representations class (LFR) parameters are LFR(unprivileged_groups, privileged_groups, k=5, Ax=0.01, Ay=1.0, Az=50.0, print_interval=250, verbose=0, seed=None). The only required parameters are unprivileged_groups and privileged_groups, so I have used LFR = preprocessing.LFR(unprivileged_groups, privileged_groups) to initialize the LFR.

However, when I run the LFR fit_transform and then retrain and test the model (sklearn's Random Forest) on the updated dataset, It now only predicts positive outcomes for every single datapoint. I have played around with the initialization of LFR, but no changes to that have made a difference.

Do you have any suggestions on how to fix the LFR so that it doesn't predict only positive outcomes?

Brianjoroge commented 3 years ago

Got the same problem too. Following closely...

REncona commented 2 years ago

Hi, was this issue resolved? I'm facing the same problem