AWS Certified AI Practitioner Practice Exam

Which of the following methods can reduce bias in a machine learning model?

1. A. Implementing regularization techniques

2. B. Using synthetic data generation

3. C. Running data profiling

4. D. Decreasing model complexity

The correct answer is: B. Using synthetic data generation

Using synthetic data generation is a powerful method to reduce bias in a machine learning model because it can help address issues related to data imbalance or underrepresentation of certain classes in the training dataset. By generating synthetic data, you can create additional samples that reflect the characteristics of the underrepresented classes, thereby providing the model with a more balanced and comprehensive view of the dataset. This can lead to improved model performance, particularly in scenarios where the original data does not adequately capture the diversity of possible inputs. This approach can enhance the model's ability to generalize and make accurate predictions across different classes, ultimately reducing bias that could arise from a skewed training dataset. Synthetic data can also be tailored to include variations and scenarios that may not be present in the original data, further enriching the training experience for the model. Other methods, while beneficial in their own right, may not directly address the issue of bias in the same explicit manner that synthetic data generation does. For instance, regularization techniques aim to prevent overfitting rather than specifically targeting bias, running data profiling is more about understanding and assessing data quality than directly mitigating bias, and decreasing model complexity can sometimes lead to underfitting, which may also not resolve bias-related issues effectively.