Cited by 54Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Data Visualization and Analytics

Silva: Interactively Assessing Machine Learning Fairness Using Causality

Yan, Jing Nathan, Gu, Ziwei, Lin, Hubert, Rzeszotarski, Jeffrey M.·2020·Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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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Crossref · 10.1145/3313831.3376447

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Authors
Yan, J. N., Gu, Z., Lin, H., & Rzeszotarski, J. M.
Affiliations
Cornell University (US)
Venue
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2020-04-21
Pages
1-13
OpenAlex
W3031923829
Cited by
54
References
56
Citations by year
2026: 4 · 2025: 9 · 2024: 13 · 2023: 8 · 2022: 12 · 2021: 7 · 2020: 1
FWCI
7.26
Type
conference paper
Language
en
Open access
closed
Locations
CHI Conference on Human Factors in Computing Systems (publishedVersion)
Funders
National Science Foundation (1850195)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
Keywords
algorithmic fairness, algorithmic bias, causal inference, interactive data exploration, fairness metrics, bias sources

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