Fairness in face recognition in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bias Sources in Face Recognition

Which of the following is the most common source of bias in face recognition systems?

A Using a high learning rate during training

B Applying too many convolutional layers in the neural network

C Unequal representation of demographic groups in the training data

D Using grayscale images instead of color images

💡 Hint

Think about what causes the model to perform differently on various groups.

Bias often arises because some groups have fewer examples in the training data, causing the model to learn less about them.

❓ Metrics

intermediate

Evaluating Fairness Metrics

You have a face recognition model tested on two groups: Group A and Group B. The false positive rate (FPR) for Group A is 2%, and for Group B is 10%. What does this indicate about the model's fairness?

A The model is biased against Group B because it has a higher false positive rate

B The model is fair because both FPRs are below 15%

C The model is biased against Group A because it has a lower false positive rate

D The false positive rate does not relate to fairness

💡 Hint

Higher false positive rates mean more mistakes for that group.

A higher false positive rate for Group B means the model wrongly identifies more people from Group B, indicating bias against that group.

❓ Predict Output

advanced

Output of Face Embedding Normalization

What is the output of the following Python code snippet that normalizes face embeddings?

Computer Vision

import numpy as np
embeddings = np.array([[3, 4], [0, 0], [1, 1]])
norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
normalized = embeddings / norms
print(np.round(normalized, 2))

A

[[0.6 0.8]
 [0.  0. ]
 [0.71 0.71]]

B

[[0.6 0.8]
 [nan nan]
 [0.71 0.71]]

C

[[0.6 0.8]
 [0.  0. ]
 [0.5 0.5]]

D

[[0.6 0.8]
 [nan nan]
 [0.5 0.5]]

💡 Hint

Consider what happens when dividing by zero norm.

The second embedding is [0,0], so its norm is zero, causing division by zero and resulting in NaN values.

❓ Model Choice

advanced

Choosing a Model Architecture to Reduce Bias

Which model architecture choice is best to help reduce bias in face recognition across diverse populations?

A A simple logistic regression model trained on raw pixel values

B A deep CNN trained only on a large dataset from one ethnicity

C A deep CNN trained on unbalanced data without any fairness considerations

D A shallow CNN trained on a balanced dataset with demographic labels used for fairness constraints

💡 Hint

Think about dataset balance and fairness-aware training.

Using a balanced dataset and incorporating fairness constraints helps the model learn equally across groups, reducing bias.

🔧 Debug

expert

Debugging Disparate Impact in Face Recognition

A face recognition system shows 90% accuracy on Group X and 60% accuracy on Group Y. The training data is balanced. Which of the following is the most likely cause?

A The model uses a loss function that does not penalize errors differently across groups

B The model architecture is too complex for the task

C The training data contains mislabeled images for Group X only

D The model was trained with early stopping

💡 Hint

Consider how the loss function affects learning fairness.

If the loss function treats all errors equally without group awareness, the model may still perform worse on some groups despite balanced data.

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