A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, a

Question

Options

• AFocusing on collecting only quantitative feedback data in future surveys.
• BEnsuring that the AI model is trained on a large and diverse dataset.
• CImplementing strong access controls to limit which teams can view the raw survey data.
• DApplying data anonymization techniques to remove or obscure sensitive data.

• A
  5% (1)
• B
  5% (1)
• C
  14% (3)
• D
  76% (16)

Explanation

The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model. According to Google's Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification. Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model's parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application's outputs. This is a crucial early step in the ML lifecycle for datasets containing sensitive information.

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