Bias Detection with SageMaker Clarify
Explore how SageMaker Clarify enhances model transparency by detecting bias and providing interpretability using SHAP values. Understand key fairness metrics such as demographic parity and disparate impact applied before and after model training. Learn how to monitor bias in production systems to ensure ongoing fairness and compliance with regulations while addressing data drift and model accuracy differences.
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SageMaker Clarify transforms machine learning from a non-transparent heuristic into an auditable system by addressing two critical pillars: fairness and interpretability. Clarify computes feature attribution using SHAP (SHapley Additive exPlanations) values, quantifying each feature’s contribution to individual predictions. This transforms opaque models into auditable decision systems.
Bias detection using Clarify
For bias detection, Clarify evaluates metrics across sensitive attributes such as age, gender, or ethnicity. Key metrics include demographic parity (equal positive prediction rates across groups), equalized odds (equal true positive and false positive rates), and disparate impact ratio (the ratio of favorable outcomes between groups). These metrics are computed both pretraining on raw datasets and ...