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AI Fairness: from Principles to Practice

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AI Fairness: from Principles to Practice. / Bateni, Arash; Chan, Matthew; Eitel-Porter, Ray.
In: arXiv, 20.07.2022.

Research output: Contribution to Journal/MagazineJournal article

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Bateni A, Chan M, Eitel-Porter R. AI Fairness: from Principles to Practice. arXiv. 2022 Jul 20.

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Bateni, Arash ; Chan, Matthew ; Eitel-Porter, Ray. / AI Fairness : from Principles to Practice. In: arXiv. 2022.

Bibtex

@article{23a9702438a6477798376f3851d4db84,
title = "AI Fairness: from Principles to Practice",
abstract = "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.",
keywords = "Artificial intelligence (AI), Fairness",
author = "Arash Bateni and Matthew Chan and Ray Eitel-Porter",
year = "2022",
month = jul,
day = "20",
language = "English",
journal = "arXiv",
issn = "2331-8422",

}

RIS

TY - JOUR

T1 - AI Fairness

T2 - from Principles to Practice

AU - Bateni, Arash

AU - Chan, Matthew

AU - Eitel-Porter, Ray

PY - 2022/7/20

Y1 - 2022/7/20

N2 - 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.

AB - 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.

KW - Artificial intelligence (AI)

KW - Fairness

M3 - Journal article

JO - arXiv

JF - arXiv

SN - 2331-8422

ER -