Fairness in ML Systems

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:

1. Resampling techniques:

   1. 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.

   2. Hybrid approaches:

      1. SMOTE + Edited Nearest Neighbors (ENN): It is a combined approach where SMOTE is followed by ENN to remove noisy or borderline samples. This helps reduce overfitting by ensuring that synthetic samples align better with the minority class distribution.

   3. Alternative generative techniques: Use generative models such as GANs (generative adversarial networks) to create synthetic samples for the minority class. GANs can generate more realistic and diverse examples, which is useful for complex datasets.

2. Algorithmic adjustments:

   1. Implement cost-sensitive learning, where misclassifications of the minority class are penalized more heavily than the majority class. This encourages the algorithm to focus on correctly classifying minority instances.

3. Fairness constraints in practice:

   1. Fairness constraints in loss functions:

      1. Example: Equalized odds requires that the model achieves similar false positive and false negative rates across different groups.

   2. In-processing techniques:

      1. Adversarial debiasing: Train an ...