Reply by: MLEngineer_Banking
In financial industry we face this issue regularly. One approach is to remove sensitive attributes like gender, race etc from features. But this doesnt always work because proxy variables can still encode bias. Better approach is to use fairness metrics like demographic parity or equalized odds as additional objectives during model training. Libraries like Fairlearn and AIF360 provide tools for this.
Reply by: ResponsibleAI_Advocate
This is very important topic. First step is to analyze your training data for representation of different groups. Check if certain demographics are underrepresented. Then evaluate model performance separately for each group - accuracy, false positive rate, false negative rate should be similar across groups. If you find disparities, techniques like reweighing samples, adversarial debiasing, or fairness constraints during training can help.
User: AIethics_Researcher
Working on a ML model for loan approval prediction but concerned about potential bias against certain demographic groups. How do you guys test for bias in your models and what techniques can be used to make models more fair?
