NCA-GENL Trustworthy AI Practice Question
A retail company is deploying an LLM-based chatbot that answers customer questions about product warranties. The legal team requires that the chatbot never provides legally binding interpretations of warranty terms. Which Trustworthy AI principle is primarily addressed by implementing a content filter that blocks responses containing definitive legal advice?
⚠ Common exam trap
The trap here is equating content filtering with explainability or accountability, when its primary purpose is preventing harmful outputs, which is safety.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Safety
Safety is the Trustworthy AI principle that ensures AI systems avoid causing harm. By filtering out legally binding warranty interpretations, the chatbot avoids potential legal and financial harm to customers and the company. The other principles address different aspects: accountability is about responsibility, robustness about resilience, and explainability about understanding model decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Accountability
Why it's wrong here
Accountability focuses on assigning responsibility for AI outcomes, such as having a human owner or audit trail. While relevant to governance, it does not directly describe the act of filtering out legally binding statements. The scenario is about preventing certain content from being generated, which maps to a different principle.
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Robustness
Why it's wrong here
Robustness refers to a system's ability to withstand errors, adversarial inputs, or distribution shifts. A content filter for legal advice is not about making the model more resilient to attacks or failures; it is about constraining the scope of acceptable outputs. Therefore, robustness is not the primary principle illustrated here.
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Explainability
Why it's wrong here
Explainability concerns the ability to understand and interpret how a model arrives at its outputs. A content filter does not provide explanations; it simply prevents certain responses. While explainability is important for trust, it does not describe the action of blocking legal advice, which is a safety measure.
- ✓
Safety
Why this is correct
Safety in Trustworthy AI means ensuring the system does not cause harm, including legal or financial harm from inappropriate advice. Blocking legally binding interpretations directly prevents potential harm to customers and the company. This aligns with the safety principle, which encompasses avoiding unintended consequences and restricting the model to safe, approved behaviors.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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