Fairness metrics in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of fairness metrics in machine learning?

Fairness metrics help us check if a model treats different groups of people equally, avoiding bias and unfair treatment.

beginner

Explain 'Demographic Parity' as a fairness metric.

Demographic Parity means the model should give positive outcomes equally across different groups, regardless of their group membership.

intermediate

What does 'Equalized Odds' measure in fairness?

Equalized Odds checks if the model has equal true positive rates and false positive rates for all groups, ensuring fairness in errors and correct predictions.

intermediate

Define 'Predictive Parity' in fairness metrics.

Predictive Parity means the model's positive predictions should be equally accurate for all groups, so the chance that a positive prediction is correct is the same.

advanced

Why can it be difficult to satisfy all fairness metrics at once?

Because some fairness metrics can conflict with each other, improving one might make another worse. This is called the fairness trade-off.

Which fairness metric requires equal positive prediction rates across groups?

A Demographic Parity

B Equalized Odds

C Predictive Parity

D Calibration

Demographic Parity means the model should give positive outcomes at the same rate for all groups.

Equalized Odds ensures equal rates of which of the following across groups?

A Positive prediction values

B Overall accuracy

C True positive and false positive rates

D Negative prediction values

Equalized Odds requires equal true positive rates and false positive rates for all groups.

Predictive Parity focuses on equalizing what aspect of model predictions?

A The accuracy of positive predictions

B The proportion of positive predictions

C The false negative rate

D The overall error rate

Predictive Parity means the chance that a positive prediction is correct should be equal across groups.

What is a common challenge when applying multiple fairness metrics simultaneously?

A They always improve model accuracy

B They require no data preprocessing

C They guarantee fairness without tuning

D They can conflict and cause trade-offs

Fairness metrics can conflict, so improving one may worsen another, creating trade-offs.

Which fairness metric is concerned with equalizing error rates between groups?

A Demographic Parity

B Equalized Odds

C Predictive Parity

D Statistical Parity

Equalized Odds focuses on equalizing true positive and false positive error rates across groups.

Describe three common fairness metrics used to evaluate machine learning models and explain what each one measures.

Think about how each metric checks fairness from a different angle: outcomes, errors, or prediction accuracy.

Explain why it might be impossible to satisfy all fairness metrics at the same time in a machine learning model.

Consider how improving fairness for one group or metric can affect others negatively.

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