Data used for an object detection ML system was found to have been labelled incorrectly in many cases. Which ONE of the following options is most…

The question refers to a problem where data used for an object detection ML system was labelled

incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation:

Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model

can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly

impacts the model's accuracy, leading to poor performance because the model learns incorrect

patterns.

Why Not Other Options:

Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the

problem of incorrect data labeling.

Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of

data labeling.

Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors

or prejudices in the data rather than outright incorrect labeling.

Reference: This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under

dataset quality issues and their impact on machine learning models​​.