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

Need a 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

Need a 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

Need a 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

Need a hint?

Use a print statement with the exact text and variable.