Bias detection and fairness metrics in MLOps - Mini Project: Build & Apply

Bias Detection and Fairness Metrics

📖 Scenario: You are working on a machine learning project that predicts loan approvals. To ensure fairness, you want to detect bias in the model's predictions based on gender.

🎯 Goal: Build a simple Python script that calculates fairness metrics to detect bias in loan approval predictions by gender.

📋 What You'll Learn

Create a dictionary with exact loan approval predictions for male and female applicants

Add a configuration variable for the threshold approval rate difference

Calculate the approval rates for each gender and check if the difference exceeds the threshold

Print the fairness check result clearly

💡 Why This Matters

🌍 Real World

Detecting bias in machine learning models helps ensure fair treatment of all groups in decisions like loan approvals.

💼 Career

Understanding and implementing fairness metrics is important for ML engineers and data scientists to build ethical AI systems.

1

Create loan approval predictions data

Create a dictionary called loan_approvals with these exact entries: 'male': [1, 0, 1, 1, 0] and 'female': [1, 1, 0, 0, 0]. Here, 1 means approved and 0 means denied.

MLOps

# Create the loan_approvals dictionary with exact values
# Your code here

Hint

Use a dictionary with keys 'male' and 'female' and lists of 1s and 0s as values.

2

Set approval rate difference threshold

Create a variable called threshold and set it to 0.2. This will be the maximum allowed difference in approval rates between genders.

MLOps

loan_approvals = {'male': [1, 0, 1, 1, 0], 'female': [1, 1, 0, 0, 0]}
# Create the threshold variable and set it to 0.2
# Your code here

Hint

Use a simple variable assignment for the threshold.

3

Calculate approval rates and check fairness

Calculate the approval rate for males and females by summing their approvals and dividing by the total number of applications. Store them in male_rate and female_rate. Then calculate the absolute difference and store it in rate_diff. Finally, create a boolean variable is_fair that is True if rate_diff is less than or equal to threshold, otherwise False.

MLOps

loan_approvals = {'male': [1, 0, 1, 1, 0], 'female': [1, 1, 0, 0, 0]}
threshold = 0.2
# Calculate male_rate, female_rate, rate_diff, and is_fair
# Your code here

Hint

Use sum() and len() to calculate rates, abs() for difference, and a comparison for fairness.

4

Print fairness check result

Print the string "Fairness check passed: " followed by the value of is_fair.

MLOps

loan_approvals = {'male': [1, 0, 1, 1, 0], 'female': [1, 1, 0, 0, 0]}
threshold = 0.2
male_rate = sum(loan_approvals['male']) / len(loan_approvals['male'])
female_rate = sum(loan_approvals['female']) / len(loan_approvals['female'])
rate_diff = abs(male_rate - female_rate)
is_fair = rate_diff <= threshold
# Print the fairness check result
# Your code here

Hint

Use a print statement with the exact text and variable.

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