Fairness in face recognition in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a face recognition dataset.

Computer Vision

from sklearn.datasets import fetch_lfw_people
faces = fetch_lfw_people(min_faces_per_person=[1])

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

A100

B50

C70

D30

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing too low a number results in too few samples per person.

The parameter min_faces_per_person filters people with at least 70 images, ensuring enough data per person for fairness analysis.

2fill in blank

medium

Complete the code to split data into training and testing sets.

Computer Vision

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(faces.data, faces.target, test_size=[1], random_state=42)

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

A0.2

B0.25

C0.5

D0.3

Attempts:

3 left

💡 Hint

Common Mistakes

Using too large test size reduces training data.

A test size of 0.2 means 20% of data is for testing, which is common for balanced evaluation.

3fill in blank

hard

Fix the error in the model training code by completing the missing classifier.

Computer Vision

from sklearn.svm import [1]
model = [1](class_weight='balanced')
model.fit(X_train, y_train)

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

ALinearSVC

BSVR

CSVC

DNuSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Using SVR which is for regression, not classification.

LinearSVC supports class_weight='balanced' which helps fairness by adjusting for class imbalance.

4fill in blank

hard

Fill both blanks to compute accuracy and balanced accuracy for fairness evaluation.

Computer Vision

from sklearn.metrics import [1], [2]
acc = [1](y_test, y_pred)
bal_acc = [2](y_test, y_pred)

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

Aaccuracy_score

Bbalanced_accuracy_score

Cf1_score

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using metrics that do not handle imbalance well.

accuracy_score measures overall correctness, while balanced_accuracy_score accounts for class imbalance, important for fairness.

5fill in blank

hard

Fill all three blanks to create a dictionary of fairness metrics by group.

Computer Vision

fairness_metrics = {group: [1](y_true[group], y_pred[group]) for group in groups if len(y_true[group]) > 0}

# Use [2] to measure fairness
# Use [3] to measure overall accuracy

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

Abalanced_accuracy_score

Baccuracy_score

Cf1_score

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up metrics or using ones not suitable for group fairness.

We compute balanced_accuracy_score per group for fairness, use f1_score as a fairness-related metric, and accuracy_score for overall accuracy.

Practice

1.

What does fairness in face recognition mainly aim to achieve?

easy

A. More complex model architecture

B. Faster processing speed

C. Higher resolution images

D. Equal accuracy for all demographic groups

Solution

1. Step 1: Understand fairness goal

   Fairness means the model should work equally well for all groups, not just some.

2. Step 2: Identify fairness metric

   Accuracy or error rates should be similar across different demographic groups.

3. Final Answer:

   Equal accuracy for all demographic groups -> Option D

4. Quick Check:

   Fairness = Equal accuracy [OK]

Hint: Fairness means equal results for everyone [OK]

Common Mistakes:

• Thinking fairness means faster models
• Confusing fairness with image quality
• Assuming complex models are always fair

2.

Which of the following is the correct way to check fairness in a face recognition model?

metrics = {'group_A': 0.92, 'group_B': 0.85}
# What should we compare?

easy

A. Only check metrics['group_A']

B. Compare metrics['group_A'] and metrics['group_B'] for equality

C. Ignore metrics and check model size

D. Compare metrics['group_A'] with a random number

Solution

1. Step 1: Identify fairness check

   Fairness requires comparing performance metrics across groups.

2. Step 2: Apply comparison

   Compare accuracy or error rates between group_A and group_B to find bias.

3. Final Answer:

   Compare metrics['group_A'] and metrics['group_B'] for equality -> Option B

4. Quick Check:

   Fairness check = Compare group metrics [OK]

Hint: Compare group metrics to check fairness [OK]

Common Mistakes:

• Checking only one group
• Ignoring metrics and focusing on model size
• Comparing to unrelated values

3.

Consider this Python code snippet evaluating fairness metrics:

group_accuracies = {'A': 0.90, 'B': 0.75, 'C': 0.88}
threshold = 0.80
biased_groups = [g for g, acc in group_accuracies.items() if acc < threshold]
print(biased_groups)

What is the output?

medium

A. ['B']

B. ['A', 'B']

C. ['C']

D. []

Solution

1. Step 1: Understand the code logic

   The code collects groups with accuracy less than 0.80 into biased_groups.

2. Step 2: Check each group's accuracy

   Group A: 0.90 > 0.80 (not biased), B: 0.75 < 0.80 (biased), C: 0.88 > 0.80 (not biased)

3. Final Answer:

   ['B'] -> Option A

4. Quick Check:

   Only group B accuracy < threshold [OK]

Hint: Filter groups with accuracy below threshold [OK]

Common Mistakes:

• Including groups with accuracy above threshold
• Misreading comparison operator
• Confusing list comprehension output

4.

Find the error in this fairness evaluation code snippet:

metrics = {'group1': 0.85, 'group2': 0.80}
threshold = 0.82
biased = [g for g, v in metrics if v < threshold]
print(biased)

medium

A. Missing .items() when iterating over dictionary

B. Wrong comparison operator

C. Threshold value is too high

D. Print statement syntax error

Solution

1. Step 1: Identify dictionary iteration error

   Iterating over a dictionary directly gives keys, not key-value pairs.

2. Step 2: Fix iteration to use .items()

   Use metrics.items() to get (key, value) pairs for comparison.

3. Final Answer:

   Missing .items() when iterating over dictionary -> Option A

4. Quick Check:

   Dictionary iteration needs .items() [OK]

Hint: Use .items() to get key-value pairs from dict [OK]

Common Mistakes:

• Iterating dict keys instead of items
• Changing threshold unnecessarily
• Assuming print syntax is wrong

5.

You have a face recognition model with accuracy 0.95 on group X and 0.70 on group Y. Which approach best improves fairness?

hard

A. Ignore group Y and focus on group X

B. Increase model complexity without changing data

C. Collect more balanced training data including group Y

D. Reduce accuracy on group X to match group Y

Solution

1. Step 1: Identify fairness problem

   Model performs worse on group Y, showing bias.

2. Step 2: Choose best fairness improvement

   Balanced data helps model learn features for all groups equally.

3. Step 3: Evaluate other options

   Increasing complexity alone may not fix bias; ignoring group Y is unfair; reducing group X accuracy is not ideal.

4. Final Answer:

   Collect more balanced training data including group Y -> Option C

5. Quick Check:

   Balanced data improves fairness [OK]

Hint: Balance training data to reduce bias [OK]

Common Mistakes:

• Thinking model complexity fixes bias alone
• Ignoring underperforming groups
• Lowering accuracy on better groups