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Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial

Authors:

Kit T. Rodolfa,

Rayid GhaniAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3513 - 3514

https://doi.org/10.1145/3394486.3406708

Published: 20 August 2020 Publication History

Abstract

Tackling issues of bias and fairness when building and deploying data science systems has received increased attention from the research community in recent years, yet a lot of the research has focused on theoretical aspects and very limited set of application areas and data sets. There is a lack of 1) practical training materials, 2) methodologies, and 3) tools for researchers and developers working on real-world algorithmic decision making system to deal with issues of bias and fairness. Today, treating bias and fairness as primary metrics of interest, and building, selecting, and validating models using those metrics is not standard practice for data scientists. In this hands-on tutorial we will try to bridge the gap between research and practice, by deep diving into algorithmic fairness, from metrics and definitions to practical case studies, including bias audits using the Aequitas toolkit (http://github.com/dssg/aequitas). By the end of this hands-on tutorial, the audience will be familiar with bias mitigation frameworks and tools to help them making decisions during a project based on intervention and deployment contexts in which their system will be used.

References

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Digital Library

[3]

Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T. Rodolfa, and Rayid Ghani. Aequitas: A Bias and Fairness Audit Toolkit. (2018), nov 2018.

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Cited By

Doan TWang ZHoang NZhang WSerra ESpezzano F(2024)Fairness in Large Language Models in Three HoursProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679090(5514-5517)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679090
Criscuolo CDolci T(2024)Exploring Fairness Interpretability with FairnessFriend: A Chatbot Solution2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00037(246-253)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDEW61823.2024.00037
Asif RHassan SParr G(2023)Integrating a Blockchain-Based Governance Framework for Responsible AIFuture Internet10.3390/fi1503009715:3(97)Online publication date: 28-Feb-2023
https://doi.org/10.3390/fi15030097
Show More Cited By

Index Terms

Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial
1. Computing methodologies
  1. Machine learning
2. General and reference
  1. Cross-computing tools and techniques
    1. Evaluation

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Information

Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 Owner/Author.

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: 20 August 2020

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National Science Foundation

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Doan TWang ZHoang NZhang WSerra ESpezzano F(2024)Fairness in Large Language Models in Three HoursProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679090(5514-5517)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679090
Criscuolo CDolci T(2024)Exploring Fairness Interpretability with FairnessFriend: A Chatbot Solution2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00037(246-253)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDEW61823.2024.00037
Asif RHassan SParr G(2023)Integrating a Blockchain-Based Governance Framework for Responsible AIFuture Internet10.3390/fi1503009715:3(97)Online publication date: 28-Feb-2023
https://doi.org/10.3390/fi15030097
Bell ABynum LDrushchak NZakharchenko TRosenblatt LStoyanovich J(2023)The Possibility of Fairness: Revisiting the Impossibility Theorem in PracticeProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594007(400-422)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594007
Baresi LCriscuolo CGhezzi C(2023)Understanding Fairness Requirements for ML-based Software2023 IEEE 31st International Requirements Engineering Conference (RE)10.1109/RE57278.2023.00046(341-346)Online publication date: Sep-2023
https://doi.org/10.1109/RE57278.2023.00046
Zawad SAnwar AZhou YBaracaldo NYan F(2023)HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid57682.2023.00037(311-321)Online publication date: May-2023
https://doi.org/10.1109/CCGrid57682.2023.00037
Rawal YSoni HDani RBagchi P(2022)A Review on Service Delivery in Tourism and Hospitality Industry Through Artificial IntelligenceProceedings of Third International Conference on Computing, Communications, and Cyber-Security10.1007/978-981-19-1142-2_34(427-436)Online publication date: 3-Jul-2022
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Sharma SRawal YPal SDani R(2022)Fairness, Accountability, Sustainability, Transparency (FAST) of Artificial Intelligence in Terms of Hospitality IndustryICT Analysis and Applications10.1007/978-981-16-5655-2_48(495-504)Online publication date: 7-Jan-2022
https://doi.org/10.1007/978-981-16-5655-2_48
Flannagan C(2022)Big Data in Road Transport and Mobility ResearchAI-enabled Technologies for Autonomous and Connected Vehicles10.1007/978-3-031-06780-8_19(523-546)Online publication date: 8-Sep-2022
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Zawad SYan FAnwar A(2022)Systems Bias in Federated LearningFederated Learning10.1007/978-3-030-96896-0_12(259-278)Online publication date: 8-Feb-2022
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