Bias and fairness in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bias Types in NLP

Which of the following best describes representation bias in natural language processing?

A Bias caused by errors in tokenization or text preprocessing steps.

B Bias introduced by the model architecture that favors certain words over others.

C Bias caused by unbalanced or skewed training data that underrepresents certain groups or topics.

D Bias that occurs only during model deployment due to hardware limitations.

💡 Hint

Think about how the data itself might not fairly represent all groups or topics.

Representation bias happens when the training data does not fairly represent all groups or topics, leading the model to learn skewed patterns.

❓ Metrics

intermediate

Measuring Fairness with Equality of Opportunity

In a binary classification NLP task, which metric best captures equality of opportunity between two demographic groups?

A Difference in true positive rates (TPR) between the groups.

B Difference in overall accuracy between the groups.

C Difference in false positive rates (FPR) between the groups.

D Difference in precision between the groups.

💡 Hint

Equality of opportunity focuses on equal chances to correctly identify positive cases.

Equality of opportunity requires that the true positive rates are similar across groups, ensuring fair positive predictions.

❓ Predict Output

advanced

Output of Bias Mitigation Code Snippet

What is the output of the following Python code that applies simple bias mitigation by equalizing sample counts?

NLP

from collections import Counter

data = [('male', 'positive'), ('female', 'positive'), ('male', 'negative'), ('male', 'positive'), ('female', 'negative')]

# Count samples per gender
counts = Counter([d[0] for d in data])
min_count = min(counts.values())

# Equalize samples by truncating
balanced_data = []
counts_seen = {'male': 0, 'female': 0}
for d in data:
    gender = d[0]
    if counts_seen[gender] < min_count:
        balanced_data.append(d)
        counts_seen[gender] += 1

print(balanced_data)

A [('male', 'positive'), ('female', 'positive'), ('male', 'negative')]

B [('female', 'positive'), ('female', 'negative')]

C [('male', 'positive'), ('male', 'negative')]

D [('male', 'positive'), ('female', 'positive'), ('male', 'negative'), ('female', 'negative')]

💡 Hint

Check how many samples each gender has and how many are kept after truncation.

The code truncates each gender's samples to the minimum count (2). It keeps the first 2 samples for 'male' and 'female' respectively.

❓ Model Choice

advanced

Choosing a Model Architecture to Reduce Gender Bias

Which model architecture is best suited to reduce gender bias in a sentiment analysis task on social media text?

A A simple logistic regression model trained on raw word counts.

B A transformer model with adversarial training to remove gender information from embeddings.

C A convolutional neural network without any bias mitigation techniques.

D A recurrent neural network trained only on male-authored texts.

💡 Hint

Consider architectures that explicitly try to remove bias signals.

Adversarial training with transformers can help remove gender information from learned representations, reducing bias.

🔧 Debug

expert

Debugging Fairness Metric Calculation

Given this code snippet to compute demographic parity difference, what error or issue will it cause?

NLP

def demographic_parity_difference(preds, groups):
    # preds: list of 0/1 predictions
    # groups: list of group labels (e.g., 'A', 'B')
    group_pos_rates = {}
    for g in set(groups):
        group_preds = [p for p, grp in zip(preds, groups) if grp == g]
        group_pos_rates[g] = sum(group_preds) / len(group_preds)
    return abs(group_pos_rates['A'] - group_pos_rates['B'])

# Example usage
preds = [1, 0, 1, 1, 0]
groups = ['A', 'A', 'B', 'B', 'B']
print(demographic_parity_difference(preds, groups))

A No error; outputs 0.16666666666666663.

B ZeroDivisionError if any group has zero members.

C KeyError if groups contain labels other than 'A' or 'B'.

D TypeError because sum is used on non-numeric data.

💡 Hint

Check the inputs and how the function handles group labels.

The function calculates positive rates for groups 'A' and 'B' correctly and returns their absolute difference without error.