awesome-ml-fairness
github.com/brandeis-machine-learning/awesome-ml-fairness ↗Papers and online resources related to machine learning fairness
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SurveyBook, Blog, Case Study, and IntroductionGroup Fairness in ClassificationIndividual FairnessMinimax FairnessCounterfactual FairnessGraph MiningOnline Learning & BanditsClusteringRegressionOutlier DetectionRankingGenerationFairness and RobustnessTransfer & Federated LearningLong-term ImpactTrustworthinessAuditingEmpirical StudySoftware EngineeringLibrary & ToolkitDataset
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Counterfactual Fairness
- A Causal Framework for Discovering and Removing Direct and Indirect Discrimination
- Avoiding Discrimination through Causal Reasoning
- Causal Conceptions of Fairness and their Consequences
- Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
- Counterfactual Fairness
- Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data
Group Fairness in Classification
- Achieving Fairness at No Utility Cost via Data ReweighingPre-processing
- Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware ClassificationPre-processing
- A Fair Classifier Using Kernel Density EstimationIn-processing
- A General Approach to Fairness with Optimal TransportIn-processing
- A Reductions Approach to Fair ClassificationIn-processing
- Assessing Fairness in the Presence of Missing DataOthers
Long-term Impact
- Achieving Long-Term Fairness in Sequential Decision Making
- A Short-term Intervention for Long-term Fairness in the Labor Market
- Delayed Impact of Fair Machine Learning
- Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
- How Do Fair Decisions Fare in Long-term Qualification?
Auditing
Minimax Fairness
- Active Sampling for Min-Max Fairness
- Adaptive Sampling for Minimax Fair Classification
- Blind Pareto Fairness and Subgroup Robustness
- Fairness Without Demographics in Repeated Loss Minimization
- Fairness without Demographics through Adversarially Reweighted Learning
- Minimax Pareto Fairness: A Multi Objective Perspective
Transfer & Federated Learning
- Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
- Ditto: Fair and Robust Federated Learning Through Personalization
- Does enforcing fairness mitigate biases caused by subpopulation shift?
- Fairness Guarantees under Demographic Shift
- Fair Transfer Learning with Missing Protected Attributes
- FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Dataset
- Adult Data SetTabular Data
- Adult Reconstruction datasetTabular Data
- America Community Survey Public Use Microdata SampleTabular Data
- Arrhythmia Data SetTabular Data
- Bank Marketing Data SetTabular Data
- CelebFaces Attributes Dataset (CelebA)Image Data
Showing a sample of 235 resources. View the full list on GitHub →