AI Fairness: from Principles to Practice
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Abstract (arXiv)
This paper summarizes and evaluates various approaches, methods, and techniques for pursuing fairness in artificial intelligence (AI) systems. It examines the merits and shortcomings of these measures and proposes practical guidelines for defining, measuring, and preventing bias in AI. In particular, it cautions against some of the simplistic, yet common, methods for evaluating bias in AI systems, and offers more sophisticated and effective alternatives. The paper also addresses widespread controversies and confusions in the field by providing a common language among different stakeholders of high-impact AI systems. It describes various trade-offs involving AI fairness, and provides practical recommendations for balancing them. It offers techniques for evaluating the costs and benefits of fairness targets, and defines the role of human judgment in setting these targets. This paper provides discussions and guidelines for AI practitioners, organization leaders, and policymakers, as well as various links to additional materials for a more technical audience. Numerous real-world examples are provided to clarify the concepts, challenges, and recommendations from a practical perspective.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 20 Jul 2022.
Builds on
16 of 39 references.
References · 16
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
- 2017Algorithmic decision making and the cost of fairnesscited by 402
- 2021FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoderscited by 75
- 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
- 2016Auditing Black-box Models for Indirect Influencecited by 41
- 2019A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spacescited by 36
- 2020Rank-Based Multi-task Learning for Fair Regressioncited by 19
- 2021Obtaining Better Static Word Embeddings Using Contextual Embedding Modelscited by 18
- 2019Fairness-Aware Neural R'eyni Minimization for Continuous Featurescited by 15
- 2020Wasserstein-based fairness interpretability framework for machine learning modelscited by 13
4 more
- 2019The Impact of Data Preparation on the Fairness of Software Systemscited by 12
- 2021DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spacescited by 5
- 2021VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representationscited by 3
- 2021Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populationscited by 1
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Citing papers per year: 2024: 1.
1 paper.