Putting Fairness Principles into Practice

Alex Beutel · Jilin Chen · Tulsee S Doshi · Allison Woodruff · Christine Luu · Pierre Kreitmann +2 more

112 citations0/0 files storedProceedings of the AAAI/ACM Conference on AI Ethics and Society

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Imbalanced Data Classification Techniques

Abstract

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road. In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues. We discuss open questions in implementing equality of opportunity and describe our fairness metric, conditional equality, that takes into account distributional differences. Further, we provide a new approach to improve on the fairness metric during model training and demonstrate its efficacy in improving performance for a real-world product.

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References

2012Fairness through awareness 2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2018Mitigating Unwanted Biases with Adversarial Learning 2017Algorithmic Decision Making and the Cost of Fairness 2013Learning Fair Representations 2019Improving Fairness in Machine Learning Systems 2010Three naive Bayes approaches for discrimination-free classification

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2024Policy advice and best practices on bias and fairness in AI 2023Algorithmic Fairness 2022In-Processing Modeling Techniques for Machine Learning Fairness: A Survey 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics 2020LiFT 2024Addressing bias in bagging and boosting regression models

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