How does a generalizer model support data minimization? Find the expert answer for the IBM Artificial Intelligence Fundamentals certification exam, with a thorough explanation focused on model generalization, data security, and responsible AI practices.
Table of Contents
Question
How does a generalizer model support data minimization?
A. By increasing model explainability
B. By making training data less specific
C. By making model outputs less identifiable
D. By learning decision boundaries of the original model
Answer
D. By learning decision boundaries of the original model
Explanation
A generalizer model supports data minimization by approximating the decision boundaries of the original model, allowing it to replicate the model’s outputs and behavior without direct or detailed access to the specific underlying training data. This process means that the new model can make similar decisions or predictions as the original but does not retain or expose individual-level details from the training set.
Minimizing the exposure or storage of original data aligns with data minimization principles—especially relevant for privacy, compliance, and responsible AI development. Instead of using the raw data, the generalizer focuses on learning the essential decision-making logic (boundaries) that the original model uses to separate different classes or outputs, thereby reducing the risk of identifiable information leakage.
Options A, B, and C relate to desirable properties of machine learning models (like explainability or identifiability) but do not reflect the fundamental mechanism by which data minimization is achieved through generalization. Understanding this process is critical for both ethical and regulatory aspects tested on the IBM Artificial Intelligence Fundamentals certification.
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