Acceptance Criteria -Accuracy, Bias, and Fairness

From The Foundation for Best Practices in Machine Learning
Technical Best Practice Guideline > Model Decision-Making > Acceptance Criteria -Accuracy, Bias, and Fairness

Acceptance Criteria - Accuracy, Bias, and Fairness


Document and define clear, narrow accuracy goals and metrics that manage the tradeoff of accuracy and explainability. Document and define the Model requirements needed to meet the Fairness & Non-Discrimination goals, as discussed more thoroughly and technically in Section 11 - Fairness & Non-Discrimination.


To (a) ensure appropriate accuracy, bias and fairness metrics for Model(s); and (b) highlight associated risks that might occur in the Product Lifecycle.

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