NCA-GENM Question 95
Select 3A data scientist is tasked with explaining the decisions of a multimodal AI system that integrates text and image inputs for medical diagnostics. The system predicts conditions based on medical images and patient descriptions. How can the data scientist utilize the multimodal model to improve explainability?
- A
Use attention maps to visualize which parts of the medical image influenced the model's decisions.
- B
Analyze the contribution of textual inputs by generating feature importance scores for keywords in the patient descriptions.
- C
Exclude either the text or image inputs to simplify the model’s behavior and make it easier to understand.
- D
Apply multimodal interpretability methods to correlate text and image inputs with the model’s output.
- E
Train a simpler monomodal model for each input type and use their outputs to explain the multimodal model.
Show answer and explanation
Correct answers: A, B, D
Explanation
Improving explainability in multimodal models often involves techniques that focus on understanding how each input modality contributes to the model's decision and how they interact. Attention maps, feature importance for textual data, and multimodal interpretability methods can provide insights into the model’s reasoning process without oversimplifying or discarding data.
- A. Correct.
Using attention maps is a common method for improving explainability in multimodal models. It helps identify which parts of the image contributed most to the prediction.
- B. Correct.
Feature importance scores for keywords in the textual input can provide insight into how the model processes and weighs the text data.
- C. Incorrect.
Excluding one of the modalities oversimplifies the model and does not align with best practices for improving explainability in multimodal models.
- D. Correct.
Multimodal interpretability methods are specifically designed to analyze how different inputs (text and images) interact to produce the model's output, improving overall explainability.
- E. Incorrect.
Training separate monomodal models can provide some insights, but it doesn’t directly explain the behavior of the multimodal model since it doesn’t capture the interaction between modalities.