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An auditor is evaluating bias in a model deployed on AWS SageMaker. Which tool should be used to generate a report on pre-training and post-training bias metrics?
SageMaker Clarify is the AWS service purpose-built to detect bias and explainability issues in ML models, and it generates both pre-training bias metrics (computed on the dataset before training) and post-training bias metrics (computed on model predictions). It integrates direct…Read full explanation
When auditing an Azure Machine Learning pipeline, which functionality within 'Responsible AI dashboard' is required to perform counterfactual analysis on model predictions?
The 'Counterfactuals' component in the Azure Responsible AI dashboard allows auditors to see how input changes impact predictions.
You are auditing a model using IBM Watson OpenScale. Which feature should be configured to detect 'Disparate Impact' to ensure regulatory compliance?
The Fairness monitor in IBM Watson OpenScale is specifically designed to detect and measure bias in machine learning models, including metrics like Disparate Impact. It evaluates whether a model's predictions disproportionately favor or disadvantage certain groups based on protec…Read full explanation
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