NCA-GENL Question 199
Select 3A company is building a generative AI model that integrates diverse forms of data, such as text, images, and sensor readings. To ensure the system is transparent, fair, and verifiable, which considerations should they prioritize?
- A
Implementing data provenance tracking to document the source and transformations of the data.
- B
Using a single dataset to simplify the integration process and reduce potential biases.
- C
Performing bias detection and mitigation across all datasets before training the model.
- D
Ensuring that the AI model outputs are interpretable and can be audited.
- E
Focusing primarily on optimizing model performance metrics, such as accuracy, over fairness.
Show answer and explanation
Correct answers: A, C, D
Explanation
Integrating diverse data forms in generative AI systems requires careful attention to transparency, fairness, and verifiability. Data provenance tracking ensures traceability and accountability, while bias detection and mitigation address fairness. Additionally, making outputs interpretable and auditable is critical for maintaining transparency and trust. Simply optimizing performance without considering fairness can lead to ethical issues and undermine the trustworthiness of the system.
- A. Correct.
Tracking data provenance helps maintain transparency by providing a record of where the data came from and how it was processed, which is critical for verifiability and accountability.
- B. Incorrect.
Relying on a single dataset may simplify integration but does not address fairness or reduce biases. Additionally, it limits the diversity of the data, which can negatively impact the model's generalization capabilities.
- C. Correct.
Bias detection and mitigation are essential for ensuring fairness in the model, as diverse datasets may introduce inherent biases that need to be addressed before training.
- D. Correct.
Making the AI model interpretable and auditable ensures transparency, allowing stakeholders to understand and verify the decisions made by the model.
- E. Incorrect.
Prioritizing performance metrics like accuracy without considering fairness can lead to biased and unfair outcomes, which goes against the principles of ethical AI design.