The root cause of the reputational loss is the model producing biased and untrustworthy outputs. The most direct and effective long-term mitigation is to improve the model's alignment with desired ethical guidelines and user expectations. Reinforcement Learning from Human Feedback (RLHF) is a state-of-the-art technique specifically designed for this purpose. It uses human-provided feedback to fine-tune the model, rewarding helpful, harmless, and unbiased responses, thereby directly addressing the source of the brand damage. This aligns with AI TRiSM principles of model reliability and trust.

B. Migrating a vector database to on-premises is an infrastructure decision for data control and security, but it does not change the model's biased behavior.

C. Deploying additional containers improves system availability and scalability, not the quality or safety of the AI-generated content.

D. Increasing GPU capacity speeds up model inference but has no impact on the underlying logic or data that leads to biased outputs.

1. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0). NIST AI 100-1. The framework's "Govern" function emphasizes creating a culture of risk management, and the "Map" function involves addressing impacts on individuals and society (Section 2.2), which directly relates to mitigating bias and reputational harm.

2. Ziegler, D. M., et al. (2019). Fine-Tuning Language Models from Human Preferences. This paper details an early implementation of RLHF, showing its effectiveness in aligning models to produce outputs preferred by humans, which includes reducing harmful or biased content (Section 1: Introduction).

3. Stanford University Human-Centered Artificial Intelligence (HAI). (2023). On the Opportunities and Risks of Foundation Models. The report discusses alignment techniques like RLHF as crucial for ensuring models behave safely and in accordance with human values (Chapter 4: Risks).