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Found 2 papers, 1 papers with code
Consistent Range Approximation for Fair Predictive Modeling
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data.
Interpretable Data-Based Explanations for Fairness Debugging
We introduce Gopher, a system that produces compact, interpretable and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
