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

↓

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.

Practice

1. What is the main purpose of fairness metrics in machine learning models?

easy

A. To improve the speed of model training

B. To check if the model treats different groups equally

C. To increase the size of the training dataset

D. To reduce the number of model parameters

Solution

1. Step 1: Understand fairness metrics

   Fairness metrics are designed to measure if a model treats different groups fairly and without bias.

2. Step 2: Compare options

   Options B, C, and D relate to performance or data size, not fairness.

3. Final Answer:

   To check if the model treats different groups equally -> Option B

4. Quick Check:

   Fairness metrics = check equal treatment [OK]

Hint: Fairness metrics check equality across groups [OK]

Common Mistakes:

• Confusing fairness with accuracy
• Thinking fairness improves training speed
• Mixing fairness with data size

2. Which of the following is a correct way to calculate demographic parity difference between two groups?

easy

A. Sum of false negatives in both groups

B. Ratio of training samples in each group

C. Difference in positive prediction rates between groups

D. Difference in model training times

Solution

1. Step 1: Recall demographic parity definition

   Demographic parity measures if the positive prediction rates are similar across groups.

2. Step 2: Evaluate options

   Difference in positive prediction rates between groups matches this definition. Options A, C, and D are unrelated to demographic parity.

3. Final Answer:

   Difference in positive prediction rates between groups -> Option C

4. Quick Check:

   Demographic parity = positive rate difference [OK]

Hint: Demographic parity = compare positive rates [OK]

Common Mistakes:

• Using false negatives instead of positive rates
• Confusing sample ratios with fairness metrics
• Mixing training time with fairness

3. Given the following confusion matrices for two groups, what is the equal opportunity difference? Group A: TP=40, FN=10, FP=20, TN=30 Group B: TP=30, FN=20, FP=15, TN=35

medium

A. 0.25

B. 0.10

C. 0.05

D. 0.20

Solution

1. Step 1: Calculate true positive rates (TPR) for both groups

   TPR = TP / (TP + FN). For Group A: 40 / (40 + 10) = 0.8. For Group B: 30 / (30 + 20) = 0.6.

2. Step 2: Find equal opportunity difference

   Equal opportunity difference = |0.8 - 0.6| = 0.20.

3. Final Answer:

   0.20 -> Option D

4. Quick Check:

   Equal opportunity difference = |TPR_A - TPR_B| = 0.20 [OK]

Hint: Equal opportunity = difference in true positive rates [OK]

Common Mistakes:

• Confusing false positive rate with true positive rate
• Not dividing by (TP + FN)
• Ignoring absolute difference

4. You have this Python code to compute statistical parity difference but it gives wrong results:

def stat_parity_diff(y_true, y_pred, group):
    pos_rate_group1 = sum(y_pred[group == 1]) / len(y_pred[group == 1])
    pos_rate_group0 = sum(y_pred[group == 0]) / len(y_pred[group == 0])
    return pos_rate_group0 - pos_rate_group1

What is the main bug?

medium

A. The difference sign is reversed; should be group1 - group0

B. Using y_true instead of y_pred for positive rates

C. Not handling division by zero if group has no samples

D. Using sum instead of mean for positive rate

Solution

1. Step 1: Understand statistical parity difference

   It is usually calculated as positive rate of group 1 minus positive rate of group 0.

2. Step 2: Check code difference calculation

   The code returns pos_rate_group0 - pos_rate_group1, which reverses the sign.

3. Final Answer:

   The difference sign is reversed; should be group1 - group0 -> Option A

4. Quick Check:

   Sign order matters in parity difference [OK]

Hint: Stat parity = group1 rate minus group0 rate [OK]

Common Mistakes:

• Mixing up group order in subtraction
• Ignoring zero division errors
• Confusing y_true and y_pred

5. You want to ensure your loan approval model is fair by equalizing false positive rates (FPR) across two groups. Given these FPRs: Group X: 0.10 Group Y: 0.25 Which action best reduces this fairness gap?

hard

A. Adjust the model threshold for Group Y to be more strict

B. Increase training data size for Group X

C. Remove Group Y samples from training

D. Use accuracy as the only metric to evaluate fairness

Solution

1. Step 1: Understand false positive rate difference

   Group Y has a higher FPR (0.25) than Group X (0.10), meaning more false alarms for Group Y.

2. Step 2: Choose action to reduce FPR gap

   Adjusting the decision threshold for Group Y to be more strict reduces false positives, lowering FPR closer to Group X.

3. Final Answer:

   Adjust the model threshold for Group Y to be more strict -> Option A

4. Quick Check:

   Threshold tuning reduces FPR gap [OK]

Hint: Higher threshold for high FPR group to reduce false positives [OK]

Common Mistakes:

• Ignoring threshold adjustment
• Removing data harms fairness
• Using accuracy alone misses fairness issues