NCA-GENM Question 216
Select 3A company is training a generative AI model using a multimodal dataset that includes both textual and visual data from users. Some of the data contains sensitive personal information. To ensure compliance with data privacy regulations while maintaining effective model performance, what steps should the company prioritize?
- A
Obtain explicit consent from users for collecting and using their data in training the model.
- B
Anonymize or pseudonymize sensitive data before using it in training to protect user identities.
- C
Focus solely on model performance and exclude privacy considerations to achieve the best results.
- D
Clearly communicate to users how their data will be used and their rights to withdraw consent.
- E
Use only publicly available datasets to avoid the need for user consent or privacy measures.
Show answer and explanation
Correct answers: A, B, D
Explanation
Balancing data privacy and data consent is critical when working with sensitive information. Companies must prioritize obtaining user consent, anonymizing sensitive data, and being transparent about data usage. These steps ensure compliance with regulations, respect for user rights, and the ethical use of AI, while still enabling effective model training.
- A. Correct.
Obtaining explicit consent from users is essential to comply with data privacy regulations and ethical AI practices. This ensures users are aware and agree to their data being used.
- B. Correct.
Anonymizing or pseudonymizing sensitive data adds a layer of protection by preventing the identification of specific users while still enabling the use of data for training.
- C. Incorrect.
Ignoring privacy considerations for the sake of model performance is not acceptable. It violates data privacy laws and ethical guidelines, making this option incorrect.
- D. Correct.
Communicating how user data will be used and their rights to withdraw consent demonstrates transparency and builds trust, which are critical aspects of data privacy.
- E. Incorrect.
Using only publicly available datasets avoids some privacy issues but limits the diversity and quality of the training data, making this an incomplete solution on its own.