Research List
Research List
Mitigation Approaches
Applied to Classification
- Agarwal et al., 2018 'A Reductions Approach to Fair Classification'
- Calmon et al., 2017 'Optimized Pre-Processing for Discrimination Prevention'
- Celis et al., 2018 'Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees'
- Feldman et al., 2015 'Certifying and Removing Disparate Impact'
- Hardt et al., 2016 'Equality of Opportunity in Supervised Learning'
- Kamiran and Calders, 2012 'Data preprocessing techniques for classification without discrimination'
- Kamiran et al., 2012 'Decision Theory for Discrimination-Aware Classification'
- Kamishima et al., 2012 'Fairness-Aware Classifier with Prejudice Remover Regularizer'
- Kearns et al., 2018 'Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness'
- Plečko and Meinshausen, 2020 'Fair Data Adaptation with Quantile Preservation'
- Pleiss et al., 2017 'On Fairness and Calibration'
- Zemel et al., 2013 'Learning Fair Representations'
- Zhang et al., 2018 'Mitigating Unwanted Biases with Adversarial Learning'
Applied to Regression
- Agarwal et al., 2019 'Fair Regression: Quantitative Definitions and Reduction-based Algorithms'
- Kusner et al., 2017 'Counterfactual Fairness'
- Plečko and Meinshausen, 2020 'Fair Data Adaptation with Quantile Preservation'
- Plečko et al., 2021 'fairadapt: Causal Reasoning for Fair Data Pre-processing'
- Yurochkin and Sun, 2020 'SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness'
- Yurochkin et al., 2020 'Training individually fair ML models with Sensitive Subspace Robustness'
Fairness Approaches
- Deng et al., 2023 'Exploring How Machine Learning Practitionaters (Try To) Use Fairness Toolkits'
- Foulds et al., 2020 'An Intersectional Definition of Fairness'
- Leslie et al., 2024 'AI Fairness in Practice'
- Pfeiffer et al., 2023 'Algorithmic Fairness in AI, an Interdisciplinary View'
- Richardson and Gilbert, 2021 'A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions'
- Varona and Suarez, 2022 'Discrimination, Bias, Fairness, and Trustworthy AI'
- Verma and Rubin, 2018 'Introduction to AI Fairness'
- Zhang et al., 2020 'Introduction to AI Fairness'