Fairness metrics in ML Python - Model Pipeline Trace

Model Pipeline - Fairness metrics

This pipeline shows how fairness metrics help us check if a machine learning model treats different groups of people fairly. It measures if the model's predictions are balanced across groups like gender or race.

Data Flow - 5 Stages

1Data in

1000 rows x 6 columns→Raw dataset with features, sensitive attribute (e.g., gender), and label→1000 rows x 6 columns

Feature1=5.1, Feature2=3.5, Gender=Male, Label=1

↓

2Preprocessing

1000 rows x 6 columns→Clean data, encode categorical variables, split sensitive attribute→1000 rows x 7 columns

Feature1=5.1, Feature2=3.5, Gender_Male=1, Gender_Female=0, Label=1

↓

3Model trains

800 rows x 7 columns→Train classifier on training set (80% of data)→Model trained to predict Label

Model learns to predict Label from features

↓

4Model predicts

200 rows x 7 columns→Predict labels on test set (20% of data)→200 rows x 1 column (predicted labels)

Predicted Label=1 for sample 1

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5Fairness metrics compute

200 rows x 2 columns (predicted label, sensitive attribute)→Calculate fairness metrics like Demographic Parity and Equal Opportunity→Summary statistics for each group

Demographic Parity difference = 0.05

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |****
0.5 |*** 
0.4 |**  
0.3 |*   
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.6	Model starts learning, loss high, accuracy low
2	0.5	0.72	Loss decreases, accuracy improves
3	0.4	0.8	Model learns important patterns
4	0.35	0.83	Training converges, loss stabilizes
5	0.33	0.85	Final epoch, good accuracy and low loss

Prediction Trace - 3 Layers

Layer 1: Input features with sensitive attribute

Layer 2: Model prediction

Layer 3: Fairness metric calculation

Model Quiz - 3 Questions

What does a Demographic Parity difference close to zero mean?

A The model ignores input features

B The model has high accuracy overall

C The model predicts positive outcomes equally across groups

D The model has high loss during training

Key Insight

Fairness metrics help us see if a model treats different groups fairly by comparing prediction rates. A model can be accurate but still unfair, so checking fairness is important to build trust and avoid bias.