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'

Tool Summaries

• Bantilan, 2017 'Themis-ML, A Fairness-Aware Machine Learning Interface for End-to-End Discrimination Discovery and Mitigation
• Bellamy et al., 2018 'AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias'
• Fairlearn, 2024 ' CorrelationRemover'