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Found 5 papers, 3 papers with code
Unprocessing Seven Years of Algorithmic Fairness
Unprocessing allows for a direct comparison of methods using different underlying models and levels of relaxation.
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within.
Promoting Fairness through Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce.
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
Although recurrent neural networks (RNNs) are state-of-the-art in numerous sequential decision-making tasks, there has been little research on explaining their predictions.
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
Hence, coupled with the lack of tools for ML practitioners, real-world adoption of bias reduction methods is still scarce.
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