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Awesome GitHub RepositoriesAlgorithmic Fairness Auditing
Tools and methodologies for detecting and quantifying biases in automated decision-making systems to ensure equitable outcomes.
Distinguishing note: This category focuses specifically on the audit and verification of fairness in automated systems, distinct from general software quality assurance or security auditing.
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Awesome Algorithmic Fairness Auditing GitHub Repositories
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- shap/shap
shap/shap
25,049View on GitHubSHAP is an explainable AI toolkit that provides a game theoretic framework for interpreting machine learning model predictions. It functions as a feature attribution engine, decomposing model outputs into the sum of individual feature effects to clarify how specific input variables influence a final decision. By assigning importance values to these inputs, the library enables users to understand the logic behind complex predictive models. The project distinguishes itself through its versatility and specialized calculation methods. It operates as a model-agnostic diagnostic library, capable of
Detects and quantifies potential biases within automated decision systems to ensure equitable outcomes across different demographic groups.
Jupyter Notebookdeep-learningexplainabilitygradient-boosting
