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Introduction to AI Fairness

Published: 08 May 2021 Publication History

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). arxiv:1810.01943
[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.
[3]
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent Trade-Offs in the Fair Determination of Risk Scores. Technical Report. https://doi.org/10.4230/LIPIcs.ITCS.2017.43 arXiv:1609.05807v2
[4]
Arvind Narayanan. 2018. Tutorial: 21 fairness definitions and their politics. In Proceedings of the Conference on Fairness, Accountability, and Transparency.
[5]
Solon Barocas Narayanan, Moritz Hardt, and Arvind Narayanan. 2018. Fairness and Machine Learning. fairmlbook.org.

Cited By

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  • (2024)“It’s Everybody’s Role to Speak Up... But Not Everyone Will”: Understanding AI Professionals’ Perceptions of Accountability for AI Bias MitigationACM Journal on Responsible Computing10.1145/36321211:1(1-30)Online publication date: 20-Mar-2024

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cover image ACM Conferences
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
May 2021
2965 pages
ISBN:9781450380959
DOI:10.1145/3411763
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: 08 May 2021

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Author Tags

  1. algorithmic fairness
  2. bias
  3. decision support
  4. discrimination-aware machine learning

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

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CHI 2025
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April 26 - May 1, 2025
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Cited By

View all
  • (2024)“It’s Everybody’s Role to Speak Up... But Not Everyone Will”: Understanding AI Professionals’ Perceptions of Accountability for AI Bias MitigationACM Journal on Responsible Computing10.1145/36321211:1(1-30)Online publication date: 20-Mar-2024

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