Bias detection and mitigation in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is bias in machine learning?

Bias in machine learning means the model unfairly favors or disfavors certain groups or outcomes, often due to unbalanced or incomplete data.

beginner

Name one common cause of bias in machine learning models.

One common cause is biased training data, where the data does not fairly represent all groups or scenarios.

beginner

What is bias mitigation?

Bias mitigation means using methods to reduce or remove unfair bias from a machine learning model to make it more fair.

intermediate

Give an example of a bias detection technique.

One example is measuring model performance separately for different groups to see if accuracy or errors differ significantly.

intermediate

What is one way to mitigate bias during model training?

One way is to balance the training data by adding more examples from underrepresented groups.

What does bias in a machine learning model usually indicate?

A The model treats some groups unfairly

B The model has perfect accuracy

C The model runs faster

D The model uses more data

Bias means unfair treatment or errors that affect some groups more than others.

Which of these is a common cause of bias?

A Balanced and diverse data

B Increasing model layers

C Using more computing power

D Biased or incomplete training data

Biased or incomplete data can cause the model to learn unfair patterns.

What is bias mitigation?

A Adding more bias to the model

B Reducing unfair bias in the model

C Making the model slower

D Ignoring bias in data

Bias mitigation means reducing unfair bias to improve fairness.

How can you detect bias in a model?

A Check if the model performs equally well for all groups

B Only look at overall accuracy

C Use less data

D Ignore group differences

Checking performance across groups helps find bias.

Which method helps mitigate bias during training?

A Removing underrepresented groups from data

B Using fewer features

C Balancing data with more examples from all groups

D Training for fewer epochs

Balancing data helps the model learn fairly from all groups.

Explain what bias in machine learning is and why it matters.

Think about how unfair treatment can happen in predictions.

Describe two ways to detect and two ways to mitigate bias in a model.

Consider both checking the model and changing data or training.

Practice

1. What is the main goal of bias detection in machine learning models?

easy

A. To find unfair treatment of certain groups by the model

B. To increase the speed of model training

C. To reduce the size of the dataset

D. To improve the model's accuracy on all data

Solution

1. Step 1: Understand bias detection purpose

   Bias detection aims to identify if a model treats some groups unfairly compared to others.

2. Step 2: Compare options to goal

   Only To find unfair treatment of certain groups by the model matches this goal; others focus on speed, size, or accuracy, which are unrelated.

3. Final Answer:

   To find unfair treatment of certain groups by the model -> Option A

4. Quick Check:

   Bias detection = find unfair treatment [OK]

Hint: Bias detection finds unfairness in model decisions [OK]

Common Mistakes:

• Confusing bias detection with improving accuracy
• Thinking bias detection speeds up training
• Mixing bias detection with data size reduction

2. Which of the following is a common technique to mitigate bias in training data?

easy

A. Oversampling underrepresented groups

B. Reducing the number of features

C. Increasing the learning rate

D. Using dropout layers in neural networks

Solution

1. Step 1: Identify bias mitigation methods

   Oversampling underrepresented groups balances data, reducing bias.

2. Step 2: Evaluate other options

   Reducing features, increasing learning rate, and dropout affect model complexity or training, not bias directly.

3. Final Answer:

   Oversampling underrepresented groups -> Option A

4. Quick Check:

   Bias mitigation = oversampling minority data [OK]

Hint: Balance data by oversampling small groups [OK]

Common Mistakes:

• Confusing feature reduction with bias mitigation
• Thinking learning rate affects bias
• Mixing dropout with bias correction

3. Consider this Python code snippet for bias mitigation:

from sklearn.utils import resample

# Original data
X = [[1], [2], [3], [4]]
y = [0, 0, 1, 1]

# Minority class samples
X_minority = [x for x, label in zip(X, y) if label == 1]
y_minority = [1, 1]

# Oversample minority class
X_upsampled, y_upsampled = resample(X_minority, y_minority, replace=True, n_samples=4, random_state=42)

print(len(X_upsampled), len(y_upsampled))

What will be the output of the print statement?

medium

A. 2 2

B. 4 4

C. 8 8

D. Error due to wrong resample usage

Solution

1. Step 1: Understand resample parameters

   resample duplicates minority samples to reach n_samples=4, so output lists have length 4.

2. Step 2: Check print output

   Both X_upsampled and y_upsampled have length 4, so print outputs '4 4'.

3. Final Answer:

   4 4 -> Option B

4. Quick Check:

   Oversampling size = 4 [OK]

Hint: resample with n_samples sets output length [OK]

Common Mistakes:

• Assuming output length stays original size
• Thinking resample doubles data always
• Expecting error from resample usage

4. You have a model that shows bias against a group. The code below tries to fix it but causes an error:

def mitigate_bias(data, labels):
    # Remove samples from majority group
    filtered_data = [d for d, l in zip(data, labels) if l != 'majority']
    filtered_labels = [l for l in labels if l != 'majority']
    return filtered_data, filtered_labels

X = [1, 2, 3, 4]
y = ['majority', 'minority', 'majority', 'minority']

X_new, y_new = mitigate_bias(X, y)
print(len(X_new), len(y_new))

What is the error in this code?

medium

A. Function returns wrong variable names

B. Syntax error in list comprehension

C. No error, code runs correctly

D. filtered_labels length does not match filtered_data length

Solution

1. Step 1: Analyze the filtering logic

   filtered_data is filtered using zip(data, labels) with condition l != 'majority', selecting elements at indices 1 and 3. filtered_labels is filtered directly from labels with the same condition, resulting in elements at indices 1 and 3. However, filtered_data and filtered_labels are filtered differently: filtered_data uses zip to filter data and labels together, but filtered_labels filters labels alone, which can cause length mismatch if data and labels are not aligned.

2. Step 2: Check lengths

   In this specific example, both filtered_data and filtered_labels have length 2, but this is coincidental. The approach is error-prone and can cause mismatches in other cases.

3. Step 3: Correct approach

   Both filtered_data and filtered_labels should be filtered using zip together to ensure matching lengths.

4. Final Answer:

   filtered_labels length does not match filtered_data length -> Option D

5. Quick Check:

   Separate filtering risks length mismatch [OK]

Hint: Filter data and labels together using zip to keep lengths equal [OK]

Common Mistakes:

• Mistakenly thinking separate comprehensions cause length mismatch
• Believing zip and direct label filter select differently
• Assuming the code has a bug without tracing execution

5. You want to reduce bias in a model predicting loan approvals. You have data with a sensitive attribute 'gender'. Which combined approach best mitigates bias while keeping model performance?

hard

A. Increase model complexity to capture more patterns

B. Remove the 'gender' attribute and train the model normally

C. Use reweighting to balance training samples and add fairness constraints during model training

D. Only oversample the majority group to increase data size

Solution

1. Step 1: Understand bias mitigation strategies

   Reweighting balances data impact; fairness constraints guide model to treat groups fairly.

2. Step 2: Evaluate other options

   Removing 'gender' may hide bias but not fix it; increasing complexity can worsen bias; oversampling majority increases imbalance.

3. Final Answer:

   Use reweighting to balance training samples and add fairness constraints during model training -> Option C

4. Quick Check:

   Combine reweighting + fairness constraints = best bias mitigation [OK]

Hint: Combine data balancing and fairness rules for best results [OK]

Common Mistakes:

• Removing sensitive attribute hides but does not fix bias
• Increasing complexity ignores bias issues
• Oversampling majority worsens bias