Fairness metrics in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the accuracy of a model.

ML Python

accuracy = correct_predictions / [1]

Drag options to blanks, or click blank then click option'

Apredictions

Bcorrect_predictions

Ctotal_predictions

Dtotal_labels

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by correct_predictions instead of total_predictions.

Using predictions instead of total count.

Accuracy is the number of correct predictions divided by the total number of predictions made.

2fill in blank

medium

Complete the code to calculate demographic parity difference between two groups.

ML Python

dp_difference = abs(rate_group1 - [1])

Drag options to blanks, or click blank then click option'

Arate_group1

Brate_group2

Ctotal_group2

Dtotal_group1

Attempts:

3 left

💡 Hint

Common Mistakes

Subtracting the same group's rate.

Using total counts instead of rates.

Demographic parity difference is the absolute difference between positive rates of two groups.

3fill in blank

hard

Fix the error in the code to compute equal opportunity difference.

ML Python

equal_opportunity_diff = tpr_group1 - [1]

Drag options to blanks, or click blank then click option'

Atpr_group2

Btpr_group1

Cfpr_group2

Dfpr_group1

Attempts:

3 left

💡 Hint

Common Mistakes

Using false positive rates instead of true positive rates.

Subtracting the same group's TPR.

Equal opportunity difference compares true positive rates (TPR) between groups.

4fill in blank

hard

Fill both blanks to calculate disparate impact ratio.

ML Python

disparate_impact = [1] / [2]

Drag options to blanks, or click blank then click option'

Apositive_rate_minority

Bpositive_rate_majority

Ctotal_minority

Dtotal_majority

Attempts:

3 left

💡 Hint

Common Mistakes

Using total counts instead of positive rates.

Reversing numerator and denominator.

Disparate impact is the ratio of positive rates between minority and majority groups.

5fill in blank

hard

Fill all three blanks to create a fairness report dictionary.

ML Python

fairness_report = {
    'demographic_parity_diff': [1],
    'equal_opportunity_diff': [2],
    'disparate_impact': [3]
}

Drag options to blanks, or click blank then click option'

Adp_difference

Bequal_opportunity_diff

Cdisparate_impact

Daccuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Including accuracy instead of fairness metrics.

Mixing up metric variable names.

The fairness report includes demographic parity difference, equal opportunity difference, and disparate impact metrics.

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