Fairness in Machine Learning

Abstract

Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteristics such as e.g. race, gender, disabilities, and sexual or political orientation. In this manuscript, we discuss some of the limitations present in the current reasoning about fairness and in methods that deal with it, and describe some work done by the authors to address them. More specifically, we show how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios. We describe how optimal transport theory can be used to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes, overcoming the limitation of most approaches that approximate fairness desiderata by imposing constraints on the lower order moments or other functions of those distributions. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria, and that enjoys strong theoretical guarantees. We introduce an approach to learn fair representations that can generalize to unseen tasks. Finally, we describe a technique that accounts for legal restrictions about the use of sensitive attributes.

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Cited by in corpus (28)

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• Improving the Fairness of Deep Generative Models without Retraining
• Achieving Equalized Odds by Resampling Sensitive Attributes
• Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
• Conditional Learning of Fair Representations
• Motif-driven Dense Subgraph Discovery in Directed and Labeled Networks
• Predicting Early Dropout: Calibration and Algorithmic Fairness Considerations
• Representative & Fair Synthetic Data
• Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
• Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design
• An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
• Fairness with Continuous Optimal Transport
• Whither Fair Clustering?
• Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal
• RAGUEL: Recourse-Aware Group Unfairness Elimination
• Machine learning in the social and health sciences
• Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy
• Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making
• Fair for All: Best-effort Fairness Guarantees for Classification
• Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space
• The Sharpe predictor for fairness in machine learning
• Fairness-Aware Online Personalization
• Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
• How Personal is Machine Learning Personalization?
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