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Found 4 papers, 3 papers with code
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes.
Evaluating Post-hoc Interpretability with Intrinsic Interpretability
Despite Convolutional Neural Networks having reached human-level performance in some medical tasks, their clinical use has been hindered by their lack of interpretability.
FAIR-FATE: Fair Federated Learning with Momentum
While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored.
FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes
We categorize different types of datapoints according to their local neighbourhood with respect to the sensitive attributes, identifying which are more difficult to learn by the classifiers.
