NCA-GENL Question 220
Select 3An organization is deploying a generative AI model for sensitive domains such as medical diagnosis. They aim to improve the trustworthiness of the model by ensuring it produces accurate, explainable, and fair outputs. Which of the following approaches using NVIDIA and other technologies can help meet this goal?
- A
Use NVIDIA NeMo Guardrails to enforce ethical AI behavior and prevent harmful or biased responses.
- B
Leverage NVIDIA Triton Inference Server to evaluate model fairness by comparing outputs across demographic groups.
- C
Incorporate explainability libraries like SHAP or LIME to provide users with insights into the model's decision-making process.
- D
Use NVIDIA TensorRT to optimize model performance for higher throughput during inference.
- E
Implement synthetic data generation tools to create a balanced training dataset that reduces bias in the model.
Show answer and explanation
Correct answers: A, C, E
Explanation
Improving AI trustworthiness requires a combination of technologies to address fairness, explainability, and ethical behavior. NVIDIA NeMo Guardrails enforces ethical AI practices, SHAP or LIME provides users with explainability, and synthetic data generation tools contribute to fairness by balancing datasets. These approaches align with the goal of creating trustworthy AI systems for sensitive applications.
- A. Correct.
NVIDIA NeMo Guardrails can help enforce ethical AI behavior, prevent harmful outputs, and address bias, which are critical for improving AI trustworthiness.
- B. Incorrect.
While NVIDIA Triton Inference Server is a powerful tool for model deployment and serving, it does not have built-in capabilities to evaluate fairness across demographic groups.
- C. Correct.
Explainability libraries like SHAP or LIME are essential for providing insights into the model's predictions, increasing user trust and understanding.
- D. Incorrect.
NVIDIA TensorRT is focused on optimizing model performance and efficiency but does not directly address trustworthiness aspects like fairness or explainability.
- E. Correct.
Synthetic data generation can help reduce bias in the training dataset, improving fairness and trustworthiness of the AI model.