Introduction:

The assignment for my CS5100: Foundations in Artificial Intelligence class served as a walkthrough of fairness metrics and explainability in machine learning training and outputs respectively. Given the Adults data set, we did the following:

1. Preprocessed the data with an exploratory data analysis (EDA) and basic cleaning

2. Developed a Multi-Layer Perceptron (MLP) Classifier to predict income

3. Used the SHAP library to analyze feature importance and explain model predictions

4. Assessed the model using fairness metrics, including Statistical Parity Difference, Disparate Impact, and Equal Opportunity Difference. Evaluated model performance across various demographic groups, identifying any disparities or biases.

5. Implemented an in-processing bias mitigation technique (Adversarial Debiasing) to improve model fairness. Re-evaluated the model post-mitigation to assess both fairness improvements and predictive performance retention.

6. Reflected on the ethical implications of the model's predictions, especially in relation to sensitive demographic groups.
    

Results:

The plots below represent the feature weights in the MLP's before adversarial debiasing and after adversarial debiasing with respect to individuals' income predictions (> or < $50k).

Figure 1: Initially the model primarily focuses on sensitive demographic features which may lead to ethical complications.

Figure 2: After adversarial debiasing, the model primarily focuses on capital-gains and significantly reduces reliance on sensitive demographics. ​​​