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Impossibility results for fair representations
The goal of such representations is that a model trained on data under the representation (e. g., a classifier) will be guaranteed to respect some fairness constraints.
Impossibility results for fair representation
With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations.
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner.
On Learnability with Computable Learners
We initiate a study of learning with computable learners and computable output predictors.
