A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to…

Question

Options

• AModel complexity
• BTraining time
• CNumber of hyperparameters
• DDeployment time

• A
  90% (27)
• C
  3% (1)
• D
  7% (2)

Explanation

The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions. Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like "black boxes. Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes.

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