AWS Certified AI PractitionerResponsible AIMedium
A healthcare provider is implementing an AI system to assist with disease diagnosis. During data collection, it's discovered that the training dataset predominantly features data from one specific ethnic group, potentially leading to inaccurate diagnoses for other groups. What immediate step should be taken to address this Responsible AI concern?
- AReduce the size of the existing dataset to balance proportions.
- BIncrease the model's computational complexity.
- CDeploy the model with a disclaimer about its limitations.
- DEnrich the dataset with diverse demographic representation.
Show answer & explanationAnswer & explanation
Correct answer: D. Enrich the dataset with diverse demographic representation.
The problem stems from a lack of diverse representation in the training data. The most effective immediate step is to actively seek out and include data from underrepresented demographic groups to ensure the model performs accurately and fairly across all populations.
Why the other options are wrong
- A. Reducing the dataset size could lead to a less robust model overall and doesn't guarantee balanced representation, potentially discarding valuable information.
- B. Increasing computational complexity does not inherently solve data bias; it might even amplify it if the bias remains.
- C. A disclaimer does not solve the underlying issue of biased performance and could still lead to harm.
Addressing Data Bias
Addressing data bias involves identifying and mitigating systematic errors or imbalances in training data that can lead to unfair or inaccurate AI model outcomes.
- Crucial for achieving AI fairness and accuracy.
- Methods include data augmentation, re-sampling, and diverse data collection.
- Requires continuous monitoring and evaluation.
Memory trick: To fix biased data, you need to add more of what's missing, like filling in the blanks.