Insisting that users trust an AI's output by default without any human review or warnings creates a significant risk of overreliance. This practice undermines the principle of human accountability and can lead to poor business outcomes if the AI generates inaccurate, biased, or contextually inappropriate recommendations. Responsible AI governance mandates that systems, especially in high-stakes domains like sales, should augment human intelligence, not replace it without oversight. The system design should encourage critical evaluation of AI outputs.

A. While IP ownership is a valid AI risk, the scenario's primary issue is the mandated blind trust in AI outputs, not intellectual property.

C. Lack of GPU capacity is a technical resource constraint, not a governance risk stemming from the policy of how the AI is used.

D. Underfitting is a technical modeling error; the risk described is about the human-AI interaction policy, not the model's specific technical flaws.

1. Microsoft. (2023). Microsoft Responsible AI Standard, v2. "Accountability: The people who design and deploy AI systems are accountable for their operation... AI systems should be designed to allow for meaningful human control." (Page 9, Accountability Principle). The scenario violates this by removing human control and review.

2. Microsoft. (2023). Microsoft Responsible AI Standard, v2. "Reliability and Safety: AI systems should be reliable and safe... their performance should be consistent and predictable." (Page 13, Reliability and Safety Principle). Forcing trust without warnings ignores the potential for unpredictable or unreliable outputs.

3. Saleema, A., & Habib, J. (2023). A Framework for Responsible AI. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (AIES '23). https://doi.org/10.1145/3600211.3604671. This academic paper discusses the importance of human-in-the-loop systems and oversight to mitigate risks like automation bias, which is a form of overreliance.