Fairness in Machine Learning: A Survey

Abstract

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.

References in corpus (41)

• Equality of Opportunity in Supervised Learning
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
• Inherent Trade-Offs in the Fair Determination of Risk Scores
• Fairness Testing: Testing Software for Discrimination
• A Survey on the Fairness of Recommender Systems
• Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
• On Fairness and Calibration
• A Convex Framework for Fair Regression
• Fairness in Information Access Systems
• Identifying and Correcting Label Bias in Machine Learning
• Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
• On the relation between accuracy and fairness in binary classification
• Fairness in Ranking: A Survey
• Multisided Fairness for Recommendation
• Fair Regression: Quantitative Definitions and Reduction-based Algorithms
• A statistical framework for fair predictive algorithms
• Scalable Fair Clustering
• Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
• Calibrated Fairness in Bandits
• Policy Learning for Fairness in Ranking
• Marrying Fairness and Explainability in Supervised Learning
• How to be Fair and Diverse?
• Fairer and more accurate, but for whom?
• Improved Adversarial Learning for Fair Classification
• Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
• The Authority of "Fair" in Machine Learning
• The cost of fairness in classification
• A Notion of Individual Fairness for Clustering
• Survey on Fair Reinforcement Learning: Theory and Practice
• Counterfactual fairness: removing direct effects through regularization
• Fair Division Without Disparate Impact
• Can Active Learning Preemptively Mitigate Fairness Issues?
• Adaptive Sampling for Minimax Fair Classification
• CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
• FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
• FairGrad: Fairness Aware Gradient Descent
• Group Meritocratic Fairness in Linear Contextual Bandits
• Improved Approximation for Fair Correlation Clustering
• Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
• Mitigating Bias in Algorithmic Systems -- A Fish-Eye View

Cited by in corpus (55)

• Blockchain-based Digital Twins: Research Trends, Issues, and Future Challenges
• How to avoid machine learning pitfalls: a guide for academic researchers
• Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey
• A survey on datasets for fairness-aware machine learning
• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
• Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree
• Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency
• Bias and Fairness in Computer Vision Applications of the Criminal Justice System
• Achieving Model Fairness in Vertical Federated Learning
• Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation
• Modeling Techniques for Machine Learning Fairness: A Survey
• Fairness in Algorithmic Profiling: A German Case Study
• On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
• Properties of fairness measures in the context of varying class imbalance and protected group ratios
• FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
• FairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms
• A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
• Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report
• Formalising Anti-Discrimination Law in Automated Decision Systems
• Can Active Learning Preemptively Mitigate Fairness Issues?
• Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
• Adversarial Reweighting for Speaker Verification Fairness
• Adaptive Sampling for Minimax Fair Classification
• Measuring Fairness in Generative Models
• Cooperation and Fairness in Multi-Agent Reinforcement Learning
• Towards Efficient and Explainable Hate Speech Detection via Model Distillation
• Fairness Definitions in Language Models Explained
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
• Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
• Fair Clustering Using Antidote Data
• FairGridSearch: A Framework to Compare Fairness-Enhancing Models
• On the Impact of Data Quality on Image Classification Fairness
• Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross-disciplinary Ethical Algorithm Research
• Ethical Quantum Computing: A Roadmap
• Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
• Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities
• Simulating counterfactuals
• Correct-By-Construction: Certified Individual Fairness through Neural Network Training
• The Fair Game: Auditing & Debiasing AI Algorithms Over Time
• Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach
• Adaptation and Generalization for Unknown Sensitive Factors of Variations
• A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
• Fairness-aware Federated Minimax Optimization with Convergence Guarantee
• Designing Human-AI System for Legal Research: A Case Study of Precedent Search in Chinese Law
• Learning to Rank with Variable Result Presentation Lengths
• Introducing a Family of Synthetic Datasets for Research on Bias in Machine Learning
• Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
• Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints
• StackingNet: Collective Inference Across Independent AI Foundation Models
• Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction
• Deep Clustering based Fair Outlier Detection
• Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classification
• Fairness Degrading Adversarial Attacks Against Clustering Algorithms