Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

The correct answer is D. Confusion matrix. A confusion matrix is the standard tool for evaluating a classification model's predictive accuracy. It tabulates true positives, true negatives, false positives, and false negatives, enabling calculation of key metrics such as accuracy, precision, recall, and F1 score. This dire

AI Audit Planning and Execution

Question

Options

AAdversarial testing
BGroup analysis
CLatency testing
DConfusion matrix

How the community answered

(55 responses)

A
7% (4)
B
2% (1)
C
4% (2)
D
87% (48)

Explanation

A confusion matrix is the standard tool for evaluating a classification model's predictive accuracy. It tabulates true positives, true negatives, false positives, and false negatives, enabling calculation of key metrics such as accuracy, precision, recall, and F1 score. This directly quantifies whether the model is making correct predictions and where it fails. Adversarial testing (A) evaluates the model's robustness against deliberately manipulated inputs, not general prediction accuracy. Group analysis (B) is not a standard ML evaluation methodology in this context. Latency testing (C) measures response speed and computational performance, which has no bearing on whether the model's predictions are correct.

Topics

#AI Model Evaluation#Confusion Matrix#Prediction Accuracy#Audit Techniques

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