Bias detection and fairness metrics in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate demographic parity difference using the AIF360 library.

MLOps

from aif360.metrics import BinaryLabelDatasetMetric
metric = BinaryLabelDatasetMetric(dataset, privileged_groups=[1], unprivileged_groups=unprivileged_groups)

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

A[{'sex': 1}]

B[{'race': 0}]

C[{'race': 1}]

D[{'sex': 0}]

Attempts:

3 left

💡 Hint

Common Mistakes

Using unprivileged group as privileged

Confusing attribute names

Using incorrect list format

The privileged group is usually represented by 'sex': 1 (e.g., male). This is used to calculate fairness metrics like demographic parity difference.

2fill in blank

medium

Complete the code to compute equal opportunity difference metric.

MLOps

equal_opportunity_diff = metric.[1]()

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

Aequal_opportunity_difference

Bstatistical_parity_difference

Cdisparate_impact

Daverage_odds_difference

Attempts:

3 left

💡 Hint

Common Mistakes

Using statistical parity difference instead

Confusing average odds with equal opportunity

The method equal_opportunity_difference() computes the difference in true positive rates between privileged and unprivileged groups.

3fill in blank

hard

Fix the error in the code to calculate disparate impact.

MLOps

disparate_impact = metric.[1]()

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

AdisparateImpact

Bdisparate_impact

Cdisparate_impact_score

DdisparateImpactScore

Attempts:

3 left

💡 Hint

Common Mistakes

Using camelCase method names

Adding extra suffixes to method names

The correct method name is disparate_impact() with underscores and lowercase letters.

4fill in blank

hard

Fill both blanks to create a fairness metric object for unprivileged and privileged groups.

MLOps

metric = BinaryLabelDatasetMetric(dataset, privileged_groups=[1], unprivileged_groups=[2])

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

A[{'gender': 1}]

B[{'gender': 0}]

C[{'age': 1}]

D[{'age': 0}]

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping privileged and unprivileged groups

Using wrong attribute names

The privileged group is usually 'gender': 1 and unprivileged is 'gender': 0 to measure fairness across gender.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters dataset features by threshold.

MLOps

filtered_features = {feature: value for feature, value in dataset.features.items() if value [1] threshold and feature [2] 'age' and value [3] 0}

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

A>

B!=

C>=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators

Including 'age' feature by mistake

The comprehension filters features with values greater than threshold, excludes 'age' feature, and values greater or equal to zero.

Practice

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

easy

A. To improve the speed of model training

B. To find unfair treatment or discrimination in model predictions

C. To increase the size of the training dataset

D. To reduce the cost of cloud computing resources

Solution

1. Step 1: Understand bias detection context

   Bias detection focuses on identifying unfair or unequal treatment of different groups by a model.

2. Step 2: Compare options to purpose

   Only To find unfair treatment or discrimination in model predictions correctly describes bias detection as finding unfair treatment in predictions.

3. Final Answer:

   To find unfair treatment or discrimination in model predictions -> Option B

4. Quick Check:

   Bias detection = find unfair treatment [OK]

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

Common Mistakes:

• Confusing bias detection with model speed optimization
• Thinking bias detection changes dataset size
• Mixing bias detection with cost reduction

2. Which of the following is a correct way to calculate demographic parity difference in Python?

easy

A. dp_diff = abs(rate_group1 - rate_group2)

B. dp_diff = rate_group1 + rate_group2

C. dp_diff = rate_group1 * rate_group2

D. dp_diff = rate_group1 / rate_group2

Solution

1. Step 1: Understand demographic parity difference formula

   It is the absolute difference between positive outcome rates of two groups.

2. Step 2: Match formula to options

   dp_diff = abs(rate_group1 - rate_group2) correctly uses absolute difference, others use incorrect operations.

3. Final Answer:

   dp_diff = abs(rate_group1 - rate_group2) -> Option A

4. Quick Check:

   Demographic parity difference = absolute difference [OK]

Hint: Use absolute difference for parity difference [OK]

Common Mistakes:

• Using addition or multiplication instead of difference
• Forgetting to take absolute value
• Dividing rates which is not standard

3. Given the following Python code snippet, what is the output?

group1_positive_rate = 0.7
group2_positive_rate = 0.5
dp_diff = abs(group1_positive_rate - group2_positive_rate)
print(round(dp_diff, 2))

medium

A. 0.2

B. 1.20

C. 0.12

D. 0.35

Solution

1. Step 1: Calculate difference between rates

   0.7 - 0.5 = 0.2

2. Step 2: Apply absolute and rounding

   Absolute value is 0.2, rounded to 2 decimals is 0.2

3. Final Answer:

   0.2 -> Option A

4. Quick Check:

   abs(0.7 - 0.5) = 0.2 [OK]

Hint: Subtract and round absolute difference [OK]

Common Mistakes:

• Mixing up subtraction order
• Not rounding output
• Confusing decimal places

4. You wrote this code to calculate equal opportunity difference but it gives wrong results:

tpr_group1 = 0.8
tpr_group2 = 0.6
equal_opp_diff = tpr_group1 - tpr_group2
print(equal_opp_diff)

What is the likely issue?

medium

A. You need to add the true positive rates, not subtract

B. You should multiply the true positive rates instead of subtracting

C. You forgot to take the absolute value of the difference

D. The variable names are incorrect

Solution

1. Step 1: Understand equal opportunity difference metric

   It measures the absolute difference between true positive rates of groups.

2. Step 2: Check code calculation

   Code subtracts but does not take absolute value, so negative results possible.

3. Final Answer:

   You forgot to take the absolute value of the difference -> Option C

4. Quick Check:

   Equal opportunity difference = absolute difference [OK]

Hint: Always use absolute difference for fairness metrics [OK]

Common Mistakes:

• Ignoring absolute value leads to negative results
• Using addition or multiplication wrongly
• Assuming variable names cause errors

5. You want to ensure fairness in a loan approval model. The model predicts positive outcomes for two groups with rates 0.65 and 0.55. Which fairness metric and threshold would best detect bias if you want less than 10% difference between groups?

hard

A. Use accuracy score and check if difference < 0.1

B. Use recall difference and check if difference > 0.2

C. Use precision difference and check if difference > 0.1

D. Use demographic parity difference and check if difference < 0.1

Solution

1. Step 1: Identify appropriate fairness metric

   Demographic parity difference measures difference in positive prediction rates between groups.

2. Step 2: Apply threshold for bias detection

   Checking if difference is less than 0.1 (10%) ensures fairness within acceptable limits.

3. Final Answer:

   Use demographic parity difference and check if difference < 0.1 -> Option D

4. Quick Check:

   Demographic parity difference < 0.1 = fairness [OK]

Hint: Demographic parity difference < threshold detects bias [OK]

Common Mistakes:

• Using accuracy instead of fairness metrics
• Checking for difference greater than threshold incorrectly
• Confusing precision or recall with demographic parity