Silva: Interactively Assessing Machine Learning Fairness Using Causality
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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Fine Tuning involves customizing pre-trained models to specific domains or user preferences, enhancing accuracy and relevance for niche applications.
“Fine Tuning: Involves customizing pre-trained models to specific domains or user preferences, enhancing accuracy and relevance for niche applications [60], [258], [259].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [258]
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“A significant challenge in this area is dealing with intersectional biases [221], [222], [223] and understanding the causal interactions that may contribute to these biases [224], [225], [226], [227].”
BackgroundToward involving end-users in interactive human-in-the-loop AI fairness
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Zhang, C., Zhu, S., Yang, X., Tseng, Y.-C., Jiang, S., & Rzeszotarski, J. M. (2025) · Proceedings of the 7th ACM Conference on Conversational User Interfaces · doi:10.1145/3719160.3736618 · Cited by 4 · see citing works
Self-citation
- 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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