Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?
Answer(s): A
Expanding the training dataset to include a wide range of facial variations - such as different angles, expressions, lighting conditions, and accessories - improves the model’s robustness. This reduces the risk that superficial alterations in appearance can compromise the facial recognition AI system’s accuracy.
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:
Model cards provide transparent documentation of an AI model’s intended use, performance metrics, limitations, and ethical considerations. They help stakeholders understand how to use the model responsibly and ensure accountability in ethical AI deployment.
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?
Answer(s): D
Recall measures the proportion of actual positive instances that the model correctly identifies. It directly evaluates the model’s ability to capture all true positives, making it the most appropriate metric when completeness of detection is critical.
The PRIMARY reason to conduct a privacy impact assessment (PIA) on an AI system is to:
A privacy impact assessment (PIA) is conducted to systematically evaluate how personal data is collected, processed, stored, and shared by an AI system. This helps identify privacy risks, ensure compliance with data protection laws, and guide mitigation measures to protect individuals’ personal information.
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer(s): C
Bias in ML algorithms often arises from unrepresentative or homogeneous training data. By diversifying the dataset - ensuring it includes a wide range of demographics, conditions, and scenarios - the model learns more balanced patterns, reducing systematic bias and improving fairness in predictions.
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