Table of Contents

• Review and General Papers
• Measurements of Fairness
• Demonstration of Bias Phemomenon in Various Applications
  • Bias in Machine Learning Models
  • Bias in Representations
• Mitigation of Unfairness
  • Mitigation of Machine Learning Models
    • Adversarial Learning
    • Calibration
    • Incorporating Priors into Feature Attribution
    • Data Collection
    • Other Mitigation Methods
  • Mitigation of Representations
• Fairness Packages and Frameworks
• Conferences
• Other Fairness Relevant Interpretability Resources

Review and General Papers

• Fairness in Deep Learning: A Computational Perspective
• The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
• Fairness and machine learning
• A Survey on Bias and Fairness in Machine Learning
• The Frontiers of Fairness in Machine Learning
• Ensuring fairness in machine learning to advance health equity
• Mitigating Gender Bias in Natural Language Processing: Literature Review
• Fairness in Recommender Systems
• Implementations in Machine Ethics: A Survey

Measurements of Fairness

• On Formalizing Fairness in Prediction with Machine Learning
• Equality of Opportunity in Supervised Learning
• Certifying and removing disparate impact
• Does mitigating ML’s impact disparity require treatment disparity?
• Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
• Beyond Parity: Fairness Objectives for Collaborative Filtering
• 50 Years of Test (Un)fairness: Lessons for Machine Learning
• Fairness Definitions Explained
• Algorithmic Fairness
• Bias in data‐driven artificial intelligence systems—An introductory survey
• Fairness is not Static: Deeper Understanding of Long Term Fairness via Simulation Studies
• Delayed Impact of Fair Machine Learning

Demonstration of Bias Phemomenon in Various Applications

Bias in Machine Learning Models

• Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
• Deep Learning for Face Recognition: Pride or Prejudiced?
• Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
• Demographic Dialectal Variation in Social Media: A Case Study of African-American English
• Feature-Wise Bias Amplification
• ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

Bias in Representations

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Gender Bias in Contextualized Word Embeddings
• Assessing Social and Intersectional Biases in Contextualized Word Representations

Mitigation of Unfairness

Mitigation of Machine Learning Models

• Adversarial Learning
  • Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
  • Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
  • Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
  • Mitigating Unwanted Biases with Adversarial Learning
  • Adversarial Removal of Demographic Attributes from Text Data
  • Compositional Fairness Constraints for Graph Embeddings
• Calibration
  • Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
  • Equality of Opportunity in Supervised Learning
• Incorporating Priors into Feature Attribution
  • Mitigating Gender Bias in Captioning Systems
  • Incorporating priors with feature attribution on text classification
  • Women Also Snowboard: Overcoming Bias in Captioning Models
  • Learning Credible Deep Neural Networks with Rationale Regularization
• Data Collection
  • Why Is My Classifier Discriminatory?
  • Incorporating Dialectal Variability for Socially Equitable Language Identification
  • REPAIR: Removing Representation Bias by Dataset Resampling
• Other Mitigation Methods
  • Mitigating Bias in Gender, Age and Ethnicity Classification: a Multi-Task Convolution Neural Network Approach
  • InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity
  • Reducing Gender Bias in Abusive Language Detection
  • Fairness-aware Learning through Regularization Approach
  • Fairness Constraints: Mechanisms for Fair Classification
  • Penalizing Unfairness in Binary Classification
  • Fairness Constraints:A Flexible Approach for Fair Classification
  • A General Framework for Fair Regression
  • Fair Regression: Quantitative Definitions and Reduction-based Algorithms

Mitigation of Representations

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Learning Gender-Neutral Word Embeddings
• Flexibly Fair Representation Learning by Disentanglement
• Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

Fairness Packages and Frameworks

• FairMLHealth
• AI Fairness 360
• Fairlearn: Fairness in machine learning mitigation algorithms
• Fairness-comparison
• Algofairness
  • fairness: Benchmarking of fairness aware machine learning algorithms
• MEASURES
• Themis-ml
• FairML
• Black Box Auditing
• What-If Tool
• FairSight: Visual Analytics for Fairness in Decision Making
• GD-IQ: Spellcheck for Bias (code not available)
• Aequitas: Bias and Fairness Audit Toolkit
• DECAF
• REPAIR
• CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models
• ML-fairness-gym: Google’s implementation based on OpenAI’s Gym
• Fairness-indicators: Tensorflow’s Fairness Evaluation and Visualization Toolkit
• Adv-Demog-Text
• GN-GloVe
• Tensorflow Constrained Optimization
• scikit-fairness
• Mitigating Gender Bias In Captioning System
• Dataset Nutrition Label

Conferences

• FAT-: ACM Conference on Fairness, Accountability, and Transparency
• FATML: Fairness, Accountability, and Transparency in Machine Learning Workshop
• AIES: AAAI/ACM Conference on AI, Ethics, and Society

Other Fairness Relevant Interpretability Resources

• Awesome machine learning interpretability resources
• The Mythos of Model Interpretability
• Towards A Rigorous Science of Interpretable Machine Learning
• Techniques for Interpretable Machine Learning
• Methods for Interpreting and Understanding Deep Neural Networks