Fairness in face recognition in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does fairness mean in face recognition systems?

Fairness means the system works equally well for all groups of people, regardless of race, gender, or age, avoiding bias or discrimination.

beginner

Why can face recognition systems be unfair?

They can be unfair because training data might have more images of some groups than others, causing the system to perform worse on underrepresented groups.

intermediate

What is demographic parity in face recognition fairness?

Demographic parity means the system's positive prediction rates are similar across different demographic groups, like race or gender, ensuring no group is unfairly treated.

intermediate

How can we reduce bias in face recognition models?

We can reduce bias by using balanced datasets, applying fairness-aware training methods, and testing the model on diverse groups to check performance.

beginner

What is the impact of unfair face recognition systems in real life?

Unfair systems can lead to wrongful identification, privacy violations, and discrimination, affecting people's trust and safety.

What is a common cause of unfairness in face recognition systems?

A High-resolution images

B Using too many layers in the model

C Imbalanced training data

D Using grayscale images

Imbalanced training data causes the model to perform better on groups with more data, leading to unfairness.

Which fairness metric checks if error rates are similar across groups?

A Demographic parity

B Accuracy

C Precision

D Recall

Demographic parity ensures error rates are balanced across different demographic groups.

What is one way to test fairness in a face recognition model?

A Test only on the training data

B Test on diverse demographic groups

C Use only one demographic group for testing

D Ignore testing and deploy immediately

Testing on diverse groups helps identify if the model is fair or biased.

Why is fairness important in face recognition?

A To improve model speed

B To increase training time

C To reduce image size

D To avoid discrimination and errors

Fairness helps prevent discrimination and wrongful identification.

Which of these can help reduce bias in face recognition?

A Balanced datasets and fairness-aware training

B Ignoring minority groups

C Using biased datasets

D Reducing model complexity only

Balanced datasets and fairness-aware training help reduce bias.

Explain why fairness is a challenge in face recognition systems and how it can affect different groups.

Think about how training data and model errors relate to fairness.

Describe methods to improve fairness in face recognition models.

Consider both data and model training approaches.

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