AIF-C01 Question 194
Single answerA financial services company is deploying a machine learning model to detect fraudulent transactions. The team is concerned about balancing model safety and transparency. They want to ensure the model is interpretable to comply with regulatory requirements but are also focused on achieving high performance to detect fraud effectively. Which approach best balances these trade-offs?
- A
Use a black-box model like a deep neural network to maximize performance and rely on external explainability tools to meet transparency requirements.
- B
Adopt a simple linear regression model for full interpretability, even if it sacrifices some fraud detection accuracy.
- C
Implement a decision tree-based model that inherently provides transparency and tune its hyperparameters to optimize performance.
- D
Deploy an ensemble model like a random forest or gradient-boosted trees to maximize accuracy without considering interpretability.
Show answer and explanation
Correct answer: C
Explanation
Balancing model safety and transparency often involves selecting models that are inherently interpretable while still providing strong performance. Decision tree-based models, such as CART (Classification and Regression Trees), naturally offer transparency because their decision-making process can be visualized and understood, which satisfies regulatory requirements. Additionally, hyperparameter tuning can help improve their performance, making them a practical choice for scenarios requiring both interpretability and accuracy.
- A. Incorrect.
Using a black-box model like a deep neural network can achieve high performance, but it lacks inherent transparency. Adding external explainability tools introduces complexity and may not fully meet regulatory requirements.
- B. Incorrect.
A simple linear regression model is interpretable, but its performance may be insufficient for complex fraud detection tasks, making it a suboptimal choice.
- C. Correct.
A decision tree-based model strikes a balance between interpretability and performance. Decision trees are inherently transparent, and tuning their hyperparameters can optimize performance, making this the best choice for balancing safety and transparency.
- D. Incorrect.
Ensemble models like random forests or gradient-boosted trees tend to prioritize accuracy but are not inherently interpretable. This approach does not address the regulatory need for transparency.