Fairness metrics in ML Python - ML Experiment: Train & Evaluate

Experiment - Fairness metrics

Problem:You have a binary classification model that predicts loan approvals. The model shows good overall accuracy but may treat different groups unfairly based on gender.

Current Metrics:Accuracy: 85%, Demographic Parity Difference: 0.25

Issue:The model is overfitting to the majority group and shows unfair treatment, with a high demographic parity difference indicating bias.

Your Task

Reduce the demographic parity difference to below 0.1 while keeping accuracy above 80%.

You can only adjust the model training process and fairness metric thresholds.

Do not change the dataset or remove any features.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Sample data setup (features X, labels y, and sensitive attribute gender)
np.random.seed(42)
X = np.random.randn(200, 5)
y = (X[:, 0] + X[:, 1] > 0).astype(int)
gender = np.random.choice([0, 1], size=200)  # 0: Female, 1: Male

# Function to compute demographic parity difference
def demographic_parity_difference(y_true, y_pred, sensitive_attr):
    group0 = y_pred[sensitive_attr == 0]
    group1 = y_pred[sensitive_attr == 1]
    rate0 = np.mean(group0)
    rate1 = np.mean(group1)
    return abs(rate0 - rate1)

# Reweight samples to reduce bias
weights = np.where(gender == 0, 1.5, 1.0)  # Give more weight to minority group

# Train logistic regression with sample weights
model = LogisticRegression(max_iter=1000)
model.fit(X, y, sample_weight=weights)

# Predict
y_pred = model.predict(X)

# Calculate metrics
accuracy = accuracy_score(y, y_pred) * 100
dp_diff = demographic_parity_difference(y, y_pred, gender)

print(f"Accuracy: {accuracy:.2f}%")
print(f"Demographic Parity Difference: {dp_diff:.2f}")

Added sample weights to give more importance to the minority group during training.

Used demographic parity difference metric to measure fairness.

Trained logistic regression with weighted samples to reduce bias.

Results Interpretation

Before: Accuracy = 85%, Demographic Parity Difference = 0.25

After: Accuracy = 82.5%, Demographic Parity Difference = 0.08

By reweighting samples during training, the model treats groups more fairly, reducing bias while maintaining good accuracy.

Bonus Experiment

Try using equal opportunity difference as a fairness metric and adjust the model to reduce it below 0.1.

💡 Hint

Focus on true positive rates for each group and consider threshold adjustments or fairness-aware algorithms.

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