A company launched an AI model last year. One year later, the company was acquired and the entire team that developed, deployed and oversaw the model left. How can the company best maintain the model from a governance perspective going forward?

All of the following are potential benefits of using private over public LLMs EXCEPT?

CASE STUDY
Please use the following to answer the next question:
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address AI governance.
The marketing company has:
- Entered into a contract with the technology company with suitable
representations and warranties.
- Completed an impact assessment on the LLM for this intended use.
- Built technical guidance on how to measure and mitigate bias in the
LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Followed applicable regulatory requirements.
- Created specific legal statements and disclosures regarding the use
of the AI on its client's advertising.
The technology company has:
- Provided guidance and resources to developers to address
environmental concerns.
- Build technical guidance on how to measure and mitigate bias in the
LLM.
- Provided tools and resources to measure bias specific to the LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Mapped and mitigated potential societal harms and large-scale
impacts.
- Followed applicable regulatory requirements and industry standards.
- Created specific legal statements and disclosures regarding the LLM,
including with respect to IP and rights to data.
All of the following results would be considered biased outputs from this AI system EXCEPT:

CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria such as a confidence score below a threshold.
To date, the healthcare network has:
Defined its AI ethical principles
Conducted discovery to identify the intended uses and success criteria for the system Established an AI risk committee Assembled a cross-functional team with clear roles and responsibilities Created policies and procedures to document standards, workflows, timelines and risk thresholds during the project The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network ' s existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the AI solution?

Retraining an LLM can be necessary for all of the following reasons EXCEPT?