research-article

Silva: Interactively Assessing Machine Learning Fairness Using Causality

Authors:

Jing Nathan Yan,

Jeffrey M. RzeszotarskiAuthors Info & Claims

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 13

https://doi.org/10.1145/3313831.3376447

Published: 23 April 2020 Publication History

Abstract

Machine learning models risk encoding unfairness on the part of their developers or data sources. However, assessing fairness is challenging as analysts might misidentify sources of bias, fail to notice them, or misapply metrics. In this paper we introduce Silva, a system for exploring potential sources of unfairness in datasets or machine learning models interactively. Silva directs user attention to relationships between attributes through a global causal view, provides interactive recommendations, presents intermediate results, and visualizes metrics. We describe the implementation of Silva, identify salient design and technical challenges, and provide an evaluation of the tool in comparison to an existing fairness optimization tool.

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Cited By

Teixeira SNogueira AGama J(2025)Fairness Analysis in Causal Models: An Application to Public ProcurementMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_14(197-211)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74627-7_14
Vincenzi BStumpf STaylor ANakao Y(2024)Lay User Involvement in Developing Human-centric Responsible AI Systems: When and How?ACM Journal on Responsible Computing10.1145/36525921:2(1-25)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652592
Stumpf STaka ENakao YLuo LSonoda RYokota TBoratto LGena CMarras MGermanakos PPopescus E(2024)The Need for User-centred Assessment of AI Fairness and CorrectnessAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664912(523-527)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664912
Show More Cited By

Index Terms

Silva: Interactively Assessing Machine Learning Fairness Using Causality
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

10688 pages

ISBN:9781450367080

DOI:10.1145/3313831

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

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Published: 23 April 2020

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Cited By

Teixeira SNogueira AGama J(2025)Fairness Analysis in Causal Models: An Application to Public ProcurementMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_14(197-211)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74627-7_14
Vincenzi BStumpf STaylor ANakao Y(2024)Lay User Involvement in Developing Human-centric Responsible AI Systems: When and How?ACM Journal on Responsible Computing10.1145/36525921:2(1-25)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652592
Stumpf STaka ENakao YLuo LSonoda RYokota TBoratto LGena CMarras MGermanakos PPopescus E(2024)The Need for User-centred Assessment of AI Fairness and CorrectnessAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664912(523-527)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664912
Vengroff D(2024)Impact Charts: A Tool for Identifying Systematic Bias in Social Systems and DataProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658965(1187-1198)Online publication date: 3-Jun-2024
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Harrison GBryson KBamba ADovichi LBinion ABorem AUr B(2024)JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data ScientistsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642755(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642755
Kabir SLi LZhang T(2024)STILE: Exploring and Debugging Social Biases in Pre-trained Text RepresentationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642111(1-20)Online publication date: 11-May-2024
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Jafari E(2024)Artificial intelligence and learning environment: Human considerationsJournal of Computer Assisted Learning10.1111/jcal.1301140:5(2135-2149)Online publication date: 21-May-2024
https://doi.org/10.1111/jcal.13011
Ahn YWolter QDick JDick JLin Y(2024)Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems2024 IEEE Visualization in Data Science (VDS)10.1109/VDS63897.2024.00009(35-39)Online publication date: 14-Oct-2024
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Lyu YLu HLee MSchmitt GLim B(2024)IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate InequalityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.323990930:7(3749-3766)Online publication date: 1-Jul-2024
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Yan XZhou YMishra AMishra HWang B(2024)Exploring Visualization for Fairness in AI Education2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00010(1-10)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00010
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