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Found 3 papers, 0 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.
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
Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)
In this paper, we introduce attribute-annotated uncertain databases (AU-DBs) which extend the UA-DB model with attribute-level annotations that record bounds on the values of an attribute across all possible worlds.
Databases
