Google Professional Machine Learning Engineer Question 475
Select 3Google Cloud PlatformYou are designing a machine learning model to predict loan approvals for a financial institution. During testing, you discover that the model disproportionately denies loan approvals for applicants from a specific demographic group. What steps should you take to assess and address the fairness and bias issues in your AI solution before deployment?
- A
Perform a fairness analysis using tools like Google Cloud's What-If Tool to identify disparities in predictions across demographic groups.
- B
Gather additional data from the underrepresented demographic group to rebalance the dataset and retrain the model.
- C
Exclude demographic features such as race or gender from the dataset to avoid bias in the model's predictions.
- D
Implement and test post-processing techniques, such as adjusting predicted outcomes to ensure fairness for all groups.
- E
Deploy the model as is since testing is complete, and monitor for fairness issues in production.
Show answer and explanation
Correct answers: A, B, D
Explanation
Fairness and bias assessment is vital when building AI solutions, especially in sensitive domains like finance. Steps such as fairness analysis, balancing datasets, and applying post-processing techniques are essential to ensure the model treats all demographic groups equitably. Simply excluding demographic features or deploying the model without addressing known issues can lead to unintended consequences and ethical concerns.
- A. Correct.
Performing a fairness analysis is a critical step in identifying and quantifying disparities in how the model treats different demographic groups. Tools like the What-If Tool can help visualize and analyze the impact of various features on predictions.
- B. Correct.
Gathering additional data from the underrepresented demographic group can help address dataset imbalances, which are often a root cause of bias in machine learning models.
- C. Incorrect.
While removing demographic features like race or gender might seem like a solution, it can lead to unintended bias through proxy variables and hinder the ability to assess fairness effectively.
- D. Correct.
Post-processing techniques, such as adjusting predictions or thresholds, provide a way to improve fairness without retraining the model entirely and can be a practical approach in certain scenarios.
- E. Incorrect.
Deploying the model as is without addressing fairness issues is not a recommended approach, as it risks perpetuating harm and violating ethical AI principles.