NCA-GENM Question 227
Select 3A team is developing a multimodal AI system for image and text analysis. During the testing phase, they notice that the system performs poorly for specific demographic groups and misrepresents certain cultural contexts in its outputs. Which approaches can the team take to minimize bias in the AI system?
- A
Increase the diversity of the training dataset to include underrepresented groups and contexts.
- B
Adopt techniques such as adversarial debiasing or re-weighting of training samples.
- C
Reduce the size of the training dataset to simplify the model's learning process.
- D
Perform regular bias audits and involve domain experts from diverse backgrounds in the evaluation process.
- E
Use a single evaluation metric to streamline the model testing process.
Show answer and explanation
Correct answers: A, B, D
Explanation
Minimizing bias in AI systems requires strategies that address the root causes of bias, such as lack of diversity in datasets, imbalances in training, and insufficient evaluation processes. Combining diverse data, advanced debiasing techniques, and expert evaluations ensures that the AI system is fair and representative across different groups and contexts. Simplistic approaches, such as reducing dataset size or relying on a single metric, are ineffective and can lead to biased outcomes.
- A. Correct.
Increasing the diversity of the training dataset is a critical step in minimizing bias, as it ensures that the model encounters a wide range of examples during training and can generalize better across different groups.
- B. Correct.
Adopting advanced techniques like adversarial debiasing or re-weighting can help mitigate bias by directly addressing imbalances or discrimination in the learning process.
- C. Incorrect.
Reducing the size of the training dataset does not minimize bias, it can, in fact, exacerbate it by limiting the diversity of the data and making the model less robust.
- D. Correct.
Performing bias audits and involving experts from diverse backgrounds ensures that biases are regularly identified and addressed, and domain-specific knowledge is incorporated into the evaluation process.
- E. Incorrect.
Using a single evaluation metric is not sufficient for detecting and minimizing bias, as bias can manifest in various ways that require multiple evaluation metrics to capture.