AIF-C01 · Question #299
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair. Which solution meets these requirements?
The correct answer is A. Review the training data to check for biases. Include data from all demographics in the training. Reviewing the training data for biases and ensuring representation from all demographics is essential for developing a responsible and fair generative AI application, especially in sensitive domains like loan approval decisions. This approach helps reduce bias and supports…
Question
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair. Which solution meets these requirements?
Options
- AReview the training data to check for biases. Include data from all demographics in the training
- BUse a deep learning model with many hidden layers.
- CKeep the model's decision-making process a secret to protect proprietary algorithms.
- DContinuously monitor the model's performance on a static test dataset
How the community answered
(48 responses)- A73% (35)
- B15% (7)
- C4% (2)
- D8% (4)
Explanation
Reviewing the training data for biases and ensuring representation from all demographics is essential for developing a responsible and fair generative AI application, especially in sensitive domains like loan approval decisions. This approach helps reduce bias and supports ethical AI
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