course

Introduction to AI Fairness

Authors:

Yunfeng Zhang,

Rachel K. E. Bellamy,

Moninder Singh,

Q. Vera LiaoAuthors Info & Claims

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 4

https://doi.org/10.1145/3334480.3375059

Published: 25 April 2020 Publication History

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Abstract

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.

References

[1]

Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. (oct 2018). https://arxiv.org/abs/1810.01943

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[2]

Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems 29, D D Lee, M Sugiyama, U V Luxburg, I Guyon, and R Garnett (Eds.). Curran Associates, Inc., 3315--3323.

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Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. Technical Report.

Crossref

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Arvind Narayanan. 2018. Tutorial: 21 fairness definitions and their politics. In Proceedings of the Conference on Fairness, Accountability, and Transparency.

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Solon Barocas Narayanan, Moritz Hardt, and Arvind Narayanan. 2018. Fairness and Machine Learning. fairmlbook.org.

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Cited By

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Chen PWu LWang L(2023)AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and ApplicationsApplied Sciences10.3390/app13181025813:18(10258)Online publication date: 13-Sep-2023
https://doi.org/10.3390/app131810258
Kharchenko VFesenko HIlliashenko O(2022)Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and ApplicationSensors10.3390/s2213486522:13(4865)Online publication date: 27-Jun-2022
https://doi.org/10.3390/s22134865
Tolmeijer SChristen MKandul SKneer MBernstein A(2022)Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision MakingProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517732(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517732
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Introduction to AI Fairness

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Information

Published In

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

4474 pages

ISBN:9781450368193

DOI:10.1145/3334480

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2020

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CHI '20

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SIGCHI

CHI '20: CHI Conference on Human Factors in Computing Systems

April 25 - 30, 2020

HI, Honolulu, USA

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Cited By

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Chen PWu LWang L(2023)AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and ApplicationsApplied Sciences10.3390/app13181025813:18(10258)Online publication date: 13-Sep-2023
https://doi.org/10.3390/app131810258
Kharchenko VFesenko HIlliashenko O(2022)Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and ApplicationSensors10.3390/s2213486522:13(4865)Online publication date: 27-Jun-2022
https://doi.org/10.3390/s22134865
Tolmeijer SChristen MKandul SKneer MBernstein A(2022)Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision MakingProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517732(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517732
Harguess JWard C(2022)Is the Next Winter Coming for AI? Elements of Making Secure and Robust AI2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)10.1109/AIPR57179.2022.10092230(1-7)Online publication date: 11-Oct-2022
https://doi.org/10.1109/AIPR57179.2022.10092230
Ajenaghughrure ISousa SLamas D(2021)Psychophysiological Modeling of Trust In TechnologyProceedings of the ACM on Human-Computer Interaction10.1145/34597455:EICS(1-25)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3459745

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