AIF-C01 Question 183
Single answerA financial services company is building a machine learning model to predict the likelihood of loan defaults. The company must comply with regulatory requirements to ensure decisions are fair, transparent, and explainable to customers. Which approach would best address the need for transparency and explainability?
- A
Use a deep neural network model for its high accuracy, as explainability can be added later.
- B
Select a simpler model like logistic regression or decision trees, which are inherently more interpretable.
- C
Rely on model accuracy metrics like precision and recall, as transparency is secondary to performance.
- D
Apply ensemble methods like random forests or gradient boosting, as they combine multiple models for better accuracy and explainability.
Show answer and explanation
Correct answer: B
Explanation
When regulatory compliance requires transparency and explainability, selecting inherently interpretable models like logistic regression or decision trees is the best approach. These models provide clear insights into how decisions are made, satisfying the need for fairness and compliance. While more complex models may offer higher accuracy, their lack of inherent interpretability makes them less suitable for such use cases.
- A. Incorrect.
Deep neural networks are often considered 'black box' models due to their complexity, making them challenging to explain. While tools exist to improve explainability, they may not always meet strict regulatory requirements.
- B. Correct.
Simpler models such as logistic regression or decision trees are inherently interpretable, making them a better fit for scenarios where transparency and compliance are critical.
- C. Incorrect.
Accuracy metrics alone do not address the need for transparency or explainability. Regulatory requirements often prioritize fairness and interpretability over raw performance.
- D. Incorrect.
While ensemble methods can improve accuracy, they also tend to reduce interpretability. These methods are not ideal for situations where explainability is a key requirement.