NCA-GENM Question 224
Select 4An organization is developing a generative AI multimodal model for medical image analysis. During testing, they discover that the model produces biased results, favoring certain demographic groups over others. Which of the following strategies should the organization implement to minimize bias in their AI system?
- A
Diversify the training dataset to include more representative samples across all demographic groups.
- B
Apply data augmentation techniques to artificially create balanced data for underrepresented groups.
- C
Fine-tune the model using real-world data without validating its fairness metrics.
- D
Introduce fairness-aware algorithms or loss functions during the training process.
- E
Conduct regular bias audits and evaluate the model's performance across different demographic groups.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To minimize bias in AI systems, it is critical to address the issue at multiple stages of development, including dataset preparation, training, and evaluation. Strategies such as diversifying the dataset, applying data augmentation, using fairness-aware algorithms, and conducting regular bias audits are essential to ensure fairness and inclusivity in AI models. Fine-tuning without validating fairness metrics is not sufficient to address bias as it does not actively measure or mitigate it.
- A. Correct.
Diversifying the training dataset ensures that the model is trained on a representative set of data, which reduces the risk of bias toward specific groups.
- B. Correct.
Data augmentation helps to artificially balance the dataset, especially when real-world data for underrepresented groups is limited. This can help minimize bias in training.
- C. Incorrect.
Fine-tuning the model without validating fairness metrics does not address bias and may exacerbate it if the new data introduces additional biases.
- D. Correct.
Fairness-aware algorithms or loss functions during training can help penalize biased predictions and improve the fairness of the model.
- E. Correct.
Conducting regular bias audits ensures that the model's performance is continuously monitored and assessed, reducing the chances of biased results in real-world applications.