AWS Certified Cloud Practitioner

Explanation

Correct Answer: D - Measuring class imbalance and adapting the training process accordingly is essential for developing an unbiased ML model.

Why Option D is correct:

1. Class imbalance detection: When developing ML models for sensitive applications like loan allocation, it's crucial to identify if certain demographic groups are underrepresented in the training data. This imbalance can lead to biased predictions.
2. Adaptive training: Once imbalance is detected, techniques like oversampling minority classes, undersampling majority classes, or using weighted loss functions can help create a more balanced and fair model.
3. Fairness considerations: This approach directly addresses potential bias in the training data, which is a fundamental step in developing ethical AI systems.

Why other options are incorrect:

• Option A: Reducing dataset size typically worsens model performance and can amplify biases rather than reduce them.
• Option B: Ensuring consistency with historical results would perpetuate existing biases in historical loan allocation decisions, which may have been discriminatory.
• Option C: Creating separate models for each demographic group could lead to unfair treatment and potentially violate anti-discrimination laws.

Key Takeaway: To develop unbiased ML models, especially in sensitive domains like finance, it's essential to:

1. Analyze training data for imbalances
2. Apply appropriate techniques to mitigate bias
3. Continuously monitor model performance across different demographic groups
4. Consider fairness metrics alongside traditional performance metrics