AIF-C01 Question 193
Single answerA healthcare company is building a machine learning model to diagnose illnesses based on patient data. The company prioritizes patient safety and requires that the model's predictions can be understood by clinicians to ensure accountability. However, during testing, the team notices that simpler, more interpretable models like decision trees have lower accuracy compared to complex models like deep neural networks. What tradeoff should the team consider to balance safety and transparency in this scenario?
- A
Use the deep neural network for higher accuracy and rely on post-hoc interpretability methods to explain the predictions.
- B
Choose the decision tree model because it provides full transparency, even if it sacrifices some predictive accuracy.
- C
Adopt an ensemble of interpretable models to improve accuracy while retaining a level of transparency.
- D
Select the deep neural network and prioritize performance over interpretability, as accuracy is most critical for patient safety.
Show answer and explanation
Correct answer: A
Explanation
The correct answer is to use the deep neural network with post-hoc interpretability methods, as this ensures high accuracy (critical for patient safety) while adding interpretability tools to make the model's predictions understandable for clinicians. This approach strikes a balance between safety and transparency, which are both essential in this healthcare scenario.
- A. Correct.
Relying on a deep neural network with post-hoc interpretability methods allows the team to achieve high accuracy while adding interpretability tools to address transparency needs. This is a reasonable balance between safety and transparency.
- B. Incorrect.
While decision trees are inherently interpretable, using a less accurate model could compromise patient safety, which is a critical requirement in this scenario. This tradeoff does not effectively balance safety and transparency.
- C. Incorrect.
Using an ensemble of interpretable models could improve accuracy, but ensembles often sacrifice interpretability due to their complexity. This option does not fully address the transparency requirement for clinicians.
- D. Incorrect.
While prioritizing accuracy is important for safety, completely ignoring interpretability could lead to a lack of trust and accountability in the model's predictions. This does not balance the tradeoff effectively.