Search Results for author:
Found 3 papers, 1 papers with code
Learning from Uncertain Data: From Possible Worlds to Possible Models
We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity.
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
