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Found 5 papers, 5 papers with code
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
In order to demonstrate the effectiveness of our approach, we evaluated our approach on four fairness problems and 16 different ML models, and our results show a significant improvement over the baseline and existing bias mitigation techniques.
Towards Understanding Fairness and its Composition in Ensemble Machine Learning
Furthermore, studies have shown that hyperparameters influence the fairness of ML models.
Fairify: Fairness Verification of Neural Networks
In this paper, we proposed Fairify, an SMT-based approach to verify individual fairness property in neural network (NN) models.
Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline
What are the fairness impacts of the preprocessing stages in machine learning pipeline?
Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance.
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