Causal fuzzing with budgeted interventions can detect residual direct and indirect influence of unlearned data that standard attribution methods miss due to proxies, cancellations, and masking.
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FairLogue provides modular tools to quantify intersectional fairness gaps in clinical ML using extended demographic parity, equalized odds, and counterfactual methods, shown on a glaucoma surgery prediction task from All of Us data.
InsightBoard integrates synchronized multi-metric plots, correlation analysis, and group fairness indicators into TensorBoard to reveal subgroup disparities that aggregate metrics hide during model training.
citing papers explorer
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Towards Reliable Testing of Machine Unlearning
Causal fuzzing with budgeted interventions can detect residual direct and indirect influence of unlearned data that standard attribution methods miss due to proxies, cancellations, and masking.
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FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
FairLogue provides modular tools to quantify intersectional fairness gaps in clinical ML using extended demographic parity, equalized odds, and counterfactual methods, shown on a glaucoma surgery prediction task from All of Us data.
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InsightBoard: An Interactive Multi-Metric Visualization and Fairness Analysis Plugin for TensorBoard
InsightBoard integrates synchronized multi-metric plots, correlation analysis, and group fairness indicators into TensorBoard to reveal subgroup disparities that aggregate metrics hide during model training.