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Found 3 papers, 1 papers with code
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
This work presents an information-theoretic perspective to group fairness trade-offs in federated learning (FL) with respect to sensitive attributes, such as gender, race, etc.
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly.
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity
In this paper, we first demonstrate that simply querying for fairness metrics, such as statistical parity and equalized odds can leak the protected attributes of individuals to the model developers.
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