NCA-GENM Question 218
Select 3A company is developing a multimodal generative AI model for autonomous vehicles and wants to ensure its AI system is trustworthy. They aim to reduce bias in data, improve model explainability, and ensure robust model performance. Which technologies or techniques provided by NVIDIA and others can help achieve these goals?
- A
Using NVIDIA AI Workbench to preprocess and balance datasets for reducing bias
- B
Leveraging NVIDIA Triton Inference Server to monitor model drift in real-time
- C
Incorporating Explainability AI toolkits like Captum to interpret model outputs
- D
Training with NVIDIA NeMo Guardrails to guide safe and ethical behavior of AI
- E
Relying solely on increasing dataset size to improve model performance
Show answer and explanation
Correct answers: A, C, D
Explanation
Developing trustworthy AI systems requires a multi-faceted approach that includes reducing bias, ensuring explainability, and guiding ethical AI behavior. NVIDIA AI Workbench, Explainability AI tools like Captum, and NeMo Guardrails are valuable technologies that address these goals. While increasing dataset size helps to some extent, it is not sufficient alone to ensure trustworthiness.
- A. Correct.
NVIDIA AI Workbench provides tools for preprocessing, labeling, and balancing datasets, which is critical for reducing bias and ensuring fairness in AI models.
- B. Incorrect.
While NVIDIA Triton Inference Server is excellent for deployment and optimizing inference workloads, monitoring model drift is not its primary purpose.
- C. Correct.
Explainability AI toolkits like Captum provide insights into model decisions, helping developers and stakeholders understand and trust the model's behavior.
- D. Correct.
NVIDIA NeMo Guardrails is designed to ensure safe AI interactions, making it crucial for developing trustworthy AI systems with ethical considerations.
- E. Incorrect.
Increasing dataset size alone does not guarantee improvements in performance or trustworthiness. Other factors like data quality, balance, and explainability are more critical.