Fairness in ML Systems
Understand how to identify and address fairness issues in machine learning models, including dealing with imbalanced data, measuring bias across subgroups, and applying ethical mitigation techniques. Learn practical methods like resampling, adversarial debiasing, fairness constraints, and continuous monitoring to promote equitable AI systems in real-world applications.
Fairness in machine learning focuses on the real-world impact of models. In this lesson, we’ll explore fairness under data imbalance, ethical implications of biased data, and how to measure and ensure equitable treatment across diverse subgroups. Let’s begin.
Imbalanced data and fairness
You’re training a classifier for a loan approval system, but only 15% of the dataset represents applicants from underrepresented communities. Given this imbalance, leadership asks how you’ll address fairness concerns.
How can you ensure fairness in a machine learning model when dealing with imbalanced datasets?
Sample answer
Here are a few key implementations and techniques that you’ll want to cover in your answer to this topic:
Resampling techniques:
Oversampling: Generate additional samples for the minority class using methods like synthetic minority oversampling technique (SMOTE). This reduces imbalance but can lead to overfitting if not used carefully.
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