NCA-GENM Question 213
Single answerA company is training a multimodal generative AI model and has access to a large dataset containing sensitive user information. The company wants to ensure compliance with data privacy regulations while also obtaining meaningful insights from the data. Which of the following approaches best balances data privacy and the importance of data consent?
- A
Use the dataset as-is, since it was obtained from publicly available sources.
- B
Anonymize the dataset to remove personally identifiable information and obtain explicit consent from users before using their data.
- C
Encrypt the dataset and bypass user consent, as encryption ensures data security.
- D
Train the model on the dataset without modifications, but notify users afterward about their data usage.
Show answer and explanation
Correct answer: B
Explanation
Balancing data privacy with the need for data consent is critical, especially when training AI models. Anonymizing data protects user privacy by removing identifiable information, and obtaining explicit consent respects user autonomy and legal requirements. This approach minimizes legal risks and ensures ethical data handling while still enabling meaningful AI model training.
- A. Incorrect.
Using the dataset as-is, even if it was obtained from publicly available sources, may violate data privacy regulations and user consent requirements. Public availability does not equate to permission for unrestricted use.
- B. Correct.
Anonymizing the dataset to remove personally identifiable information and obtaining explicit user consent ensures compliance with data privacy laws and respects user rights. This approach balances privacy and ethical data usage.
- C. Incorrect.
While encryption secures the dataset, it does not address the need for user consent. Users must be informed and give explicit permission for their data to be used, regardless of encryption.
- D. Incorrect.
Notifying users after their data has been used fails to obtain proper consent and violates ethical and legal standards of data usage.