Introduction to AI Fairness

被引：0

作者：

Zhang, Yunfeng ^{[1
]}

Bellamy, Rachel K. E. ^{[1
]}

Liao, Q. Vera ^{[1
]}

Singh, Moninder ^{[1
]}

机构：

[1] IBM Res AI, Yorktown Hts, NY 10598 USA

来源：

EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21) | 2021年

关键词：

algorithmic fairness; bias; decision support; discrimination-aware machine learning;

D O I：

10.1145/3411763.3444998

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Today, AI is used in many high-stakes decision-making applications in which fairness is an important concern. Already, there are many examples of AI being biased and making questionable and unfair decisions. Recently, the AI research community has proposed many methods to measure and mitigate unwanted biases, and developed open-source toolkits for developers to make fair AI. This course will cover the recent development in algorithmic fairness, including the many different definitions of fairness, their corresponding quantitative measurements, and ways to mitigate biases. This course is open to beginners and is designed for anyone interested in the topic of AI fairness.

引用

页数：3

共 50 条

[41] A sociotechnical view of algorithmic fairness
Dolata, Mateusz
Feuerriegel, Stefan
Schwabe, Gerhard
INFORMATION SYSTEMS JOURNAL, 2022, 32 (04) : 754 - 818
[42] Algorithmic Fairness and Fairness Computing
Fan Z.
Meng X.
Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2023, 60 (09): : 2048 - 2066
[43] What does it mean for a clinical AI to be just: conflicts between local fairness and being fit-for-purpose?
Pruski, Michal
JOURNAL OF MEDICAL ETHICS, 2024,
[44] Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, Shira
Potash, Eric
Barocas, Solon
D'Amour, Alexander
Lum, Kristian
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 8, 2021, 2021, 8 : 141 - 163
[45] On (assessing) the fairness of risk score models
Petersen, Eike
Ganz, Melanie
Holm, Sune
Feragen, Aasa
PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, 2023, : 817 - 829
[46] Algorithmic Fairness on Graphs: Methods and Trends
Kang, Jian
Tong, Hanghang
PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 4798 - 4799
[47] Fairness and Data Protection Impact Assessments
Kasirzadeh, Atoosa
Clifford, Damian
AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, 2021, : 146 - 153
[48] Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness
Nakao, Yuri
Strappelli, Lorenzo
Stumpf, Simone
Naseer, Aisha
Regoli, Daniele
Del Gamba, Giulia
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2023, 39 (09) : 1762 - 1788
[49] The introduction of the Video Assistant Referee supports the fairness of the game-An analysis of the home advantage in the German Bundesliga
Dufner, Anna-Lena
Schuetz, Lisa-Marie
Hill, Yannick
PSYCHOLOGY OF SPORT AND EXERCISE, 2023, 66
[50] Enabling Fairness in Healthcare Through Machine Learning
Grote, Thomas
Keeling, Geoff
ETHICS AND INFORMATION TECHNOLOGY, 2022, 24 (03)

← 1 2 3 4 5 →