Which of the following MOST effectively addresses bias in generative AI models?

The correct answer is D. Fairness constraints. Fairness constraints are mathematical requirements embedded directly into the model training process to enforce equitable outcomes across demographic groups (e.g., demographic parity, equalized odds). They are the most direct and systematic mechanism for addressing bias because…

AI Security Design and Implementation

Question

Options

AData minimization
BData augmentation
CAdversarial training
DFairness constraints

How the community answered

(55 responses)

A
9% (5)
B
16% (9)
C
4% (2)
D
71% (39)

Explanation

Fairness constraints are mathematical requirements embedded directly into the model training process to enforce equitable outcomes across demographic groups (e.g., demographic parity, equalized odds). They are the most direct and systematic mechanism for addressing bias because they make fairness a hard objective during optimization. Data augmentation (B) can help by diversifying training data but does not guarantee fair outputs. Adversarial training (C) improves robustness against adversarial attacks, not bias. Data minimization (A) reduces data collection scope but does not target bias specifically.

Topics

#AI Bias#Fairness#Generative AI#Bias Mitigation

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