Which of the following will BEST reduce data bias in machine learning (ML) algorithms?

The correct answer is C. Diversifying the model training data. AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may in

AI Security Design and Implementation

Question

Options

AAdopting a more simplified model
BUtilizing unstructured data sets
CDiversifying the model training data
DSecuring the model training data

How the community answered

(31 responses)

B
3% (1)
C
94% (29)
D
3% (1)

Explanation

AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.

Topics

#Data bias#Bias mitigation#Training data#Machine learning

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