NCA-GENL Question 217
Select 3A financial institution wants to ensure their generative AI model is trustworthy by mitigating biases and improving explainability in its predictions. Which of the following technologies or practices should they adopt to achieve this using NVIDIA and other supporting tools?
- A
Utilize NVIDIA NeMo Guardrails to enforce policy compliance and prevent biased outputs.
- B
Leverage Explainable AI (XAI) frameworks to provide insights into the model’s decision-making process.
- C
Rely solely on pre-trained large language models (LLMs) without fine-tuning for domain-specific fairness.
- D
Incorporate synthetic datasets generated by NVIDIA tools to balance underrepresented data categories.
- E
Disable all regularization techniques in training to ensure the model learns freely from the dataset.
Show answer and explanation
Correct answers: A, B, D
Explanation
To improve AI trustworthiness, it is essential to use technologies like NVIDIA NeMo Guardrails for policy enforcement, Explainable AI for transparency, and synthetic datasets to address biases in training data. These approaches ensure fairness, accountability, and reliability in generative AI models, aligning with best practices for trustworthy AI development.
- A. Correct.
NVIDIA NeMo Guardrails is designed to enhance trustworthiness by enforcing policies, ensuring ethical AI responses, and preventing the generation of biased or harmful outputs.
- B. Correct.
Explainable AI (XAI) frameworks provide transparency into the AI model’s predictions, which is crucial for building trust and ensuring accountability.
- C. Incorrect.
Relying solely on pre-trained LLMs without fine-tuning does not address domain-specific biases, and therefore, is not a reliable strategy for improving AI trustworthiness.
- D. Correct.
Synthetic datasets generated with NVIDIA tools can help address biases in training data by augmenting underrepresented categories, improving fairness and model reliability.
- E. Incorrect.
Disabling regularization techniques can lead to overfitting, which negatively affects model generalization and does not contribute to improving trustworthiness.