Bias detection and mitigation in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bias Types in Machine Learning

Which of the following best describes sampling bias in a dataset?

A The model predictions are skewed because of incorrect feature scaling.

B The model favors one class over another because of imbalanced class labels during training.

C The dataset contains data points that are not representative of the real population due to how samples were collected.

D The dataset has missing values that cause errors during training.

💡 Hint

Think about how the data was gathered and if it truly reflects the whole group you want to study.

Sampling bias happens when the data collected does not fairly represent the entire population, leading to unfair or inaccurate model results.

❓ Predict Output

intermediate

Detecting Bias with Statistical Parity Difference

Given the following Python code calculating statistical parity difference, what is the printed output?

ML Python

group_0_positive_rate = 0.7
group_1_positive_rate = 0.5
statistical_parity_difference = group_0_positive_rate - group_1_positive_rate
print(f"Statistical Parity Difference: {statistical_parity_difference}")

A Statistical Parity Difference: 0.2

B Statistical Parity Difference: -0.2

C Statistical Parity Difference: 1.2

D Statistical Parity Difference: 0.0

💡 Hint

Subtract the smaller positive rate from the larger one.

Statistical parity difference is the difference between positive outcome rates of two groups. Here, 0.7 - 0.5 = 0.2.

❓ Model Choice

advanced

Choosing a Model to Mitigate Bias

You want to reduce bias in a classification task where sensitive attributes affect predictions. Which model approach is best to mitigate bias during training?

A Train a standard logistic regression without any fairness constraints.

B Use a decision tree with maximum depth to fit training data perfectly.

C Train a model only on the sensitive attribute to predict the target.

D Use an adversarial debiasing model that learns to predict while minimizing sensitive attribute information.

💡 Hint

Think about a model that tries to hide sensitive information while learning.

Adversarial debiasing trains a model to predict the target while an adversary tries to predict sensitive attributes. This reduces bias by removing sensitive info from predictions.

❓ Hyperparameter

advanced

Hyperparameter Impact on Bias Mitigation

In a fairness-aware model using a regularization term to penalize bias, which hyperparameter adjustment will most likely reduce bias?

A Increase the regularization strength to penalize bias more heavily.

B Decrease the regularization strength to allow more model flexibility.

C Increase the learning rate to speed up training.

D Decrease the batch size to improve gradient estimates.

💡 Hint

Think about how stronger penalties affect bias in the model.

Increasing regularization strength on bias penalty forces the model to reduce bias more, improving fairness.

❓ Metrics

expert

Interpreting Equalized Odds Metric

You have two groups in your dataset. The true positive rates (TPR) and false positive rates (FPR) for each group are:

• Group A: TPR=0.8, FPR=0.1
• Group B: TPR=0.6, FPR=0.1

Does this model satisfy equalized odds fairness?

A Yes, because both groups have the same false positive rate.

B No, because the true positive rates differ between groups.

C Yes, because the average of TPR and FPR is equal.

D No, because the false positive rates differ between groups.

💡 Hint

Equalized odds requires both TPR and FPR to be equal across groups.

Equalized odds means both true positive rates and false positive rates must be equal for all groups. Here, TPR differs, so fairness is not satisfied.

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