AI Fairness
Notes, references and materials on AI Fairness that I found useful and helped me in my research.
Reading List
- Trustworthy Machine Learning by Kush R. Varshney (Book)
- Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
- A Framework for Benchmarking Discrimination-Aware Models in Machine Learning
- AI Fairness: How to Measure and Reduce Unwanted Bias in Machine Learning
- Fairness-aware Learning through Regularization Approach
- Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
- Moving beyond “algorithmic bias is a data problem”
- The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
- Dissecting Racial Bias in a Credit Scoring System Experimentally Developed for the Brazilian Population
- AI Explainability 360
- AI Fairness 360
- Blog Post: A Tutorial on Fairness in Machine Learning
- CS 294: Fairness in Machine Learning
- Blog Post: Fair AI: How to Detect and Remove Bias from Financial Services AI Models
- Fairness Assessment for Artificial Intelligence in Financial Industry
- Fairlearn: A toolkit for assessing and improving fairness in AI
- AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
- The Hidden Biases in Big Data by Kate Crawford
- Sex Bias in Graduate Admissions: Data from Berkeley
- Race After Technology: Abolitionist Tools for the New Jim Code
- Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
- Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
- Inherent Trade-Offs in the Fair Determination of Risk Scores
COMPAS and Criminal Justice
- Machine Bias
- Code Review: FairML
- Code Review: Compas Analysis
- Fairness in Criminal Justice Risk Assessments: The State of the Art
Trade-offs and impossibility results
Classification, Calibration, Precision, Recall
- Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
- Inherent Trade-Offs in the Fair Determination of Risk Scores
- Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
- Attacking discrimination with smarter machine learning
- Algorithmic decision making and the cost of fairness
- The problem of Infra-marginality in Outcome Tests for Discrimination
- Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Inherent limitations of observational measures
Beyond observational measures
Causal reasoning
- Background reading: Pearl (Chapter 1--3) , Pearl (Section 4.5.3)
- Elements of Causal Inference
- On causal interpretation of race in regressions adjusting for confounding and mediating variables
Causal fairness criteria
Similarity modeling, matching
- Fairness Through Awareness
- On the (im)possibility of fairness
- Why propensity scores should not be used
Measurement, sampling
- Raw Data is an Oxymoron
- Blog Post: What's the most important thing in Statistics that's not in the textbooks
- Deconstructing Statistical Questions
- Statistics and the Theory of Measurement
- Measurement Theory and Practice: The World Through Quantification
- Survey Methodology, 2nd Edition
- Sampling: Design and Analysis
Unsupervised learning
- Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
- Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Legal and policy perspectives
- Big Data’s Disparate Impact
- It’s Not Privacy, and It’s Not Fair
- The Trouble with Algorithmic Decisions
- How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem