Bias detection and mitigation in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the demographic parity difference.

ML Python

demographic_parity_diff = abs(P(y_pred = 1 | [1] = 0) - P(y_pred = 1 | [1] = 1))

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

Atarget

Bsensitive_attribute

Cfeature

Dprediction

Attempts:

3 left

💡 Hint

Common Mistakes

Using the target variable instead of the sensitive attribute.

Using the prediction variable instead of the sensitive attribute.

The demographic parity difference measures the difference in positive prediction rates between groups defined by the sensitive attribute.

2fill in blank

medium

Complete the code to split data into training and test sets with stratification on the sensitive attribute.

ML Python

train_test_split(X, y, test_size=0.3, stratify=[1], random_state=42)

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

Asensitive_attribute

BX

Cy

Drandom_state

Attempts:

3 left

💡 Hint

Common Mistakes

Stratifying on the target variable instead of the sensitive attribute.

Not stratifying at all, causing imbalance.

Stratifying on the sensitive attribute ensures that both training and test sets have similar distributions of that attribute.

3fill in blank

hard

Fix the error in the fairness metric calculation by completing the code.

ML Python

equal_opportunity_diff = abs(TPR_[1] - TPR_{group1})

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

Agroup0

Bgroup1

Cpositive

Dnegative

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same group for both TPRs.

Using positive or negative instead of group names.

True Positive Rate (TPR) should be compared between two groups, here group0 and group1. The code needs to specify the first group in the first TPR.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters features with importance above a threshold.

ML Python

important_features = {feature: importance for feature, importance in feature_importances.items() if importance [1] [2]

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

A>

B0.05

C<

D0.1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

Using too high or too low threshold.

This comprehension selects features whose importance is greater than 0.05.

5fill in blank

hard

Fill all three blanks to create a fairness-aware model training pipeline.

ML Python

model = [1](sensitive_features=[2])
model.fit(X_train, y_train)
predictions = model.[3](X_test)

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

AFairClassifier

Bsensitive_attribute

Cpredict

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using a standard classifier without fairness parameters.

Using fit instead of predict for predictions.

FairClassifier is initialized with sensitive features, then trained and used to predict on test data.

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