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Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency
Open accessAbstract
Training datasets fundamentally impact the performance of machine learning (ML) systems. Any biases introduced during training (implicit or explicit) are often reflected in the system's behaviors leading to questions about fairness and loss of trust in the system. Yet, information on training data is rarely communicated to stakeholders. In this work, we explore the concept of data-centric explanations for ML systems that describe the training data to end-users. Through a formative study, we investigate the potential utility of such an approach, including the information about training data that participants find most compelling. In a second study, we investigate reactions to our explanations across four different system scenarios. Our results suggest that data-centric explanations have the potential to impact how users judge the trustworthiness of a system and to assist users in assessing fairness. We discuss the implications of our findings for designing explanations to support users’ perceptions of ML systems.
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“Practical approaches to implement such ideas typically revolve around the systematic collection and curation of data on various aspects of systems [2, 28, 46].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [2]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Work on AI safety and reproducibility has long pointed out the crucial importance of training data for understanding model performance, ensuring reproducibility, and assessing legal exposure [2, 25, 29, 46, 58].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [2]
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“For instance, previous studies have shown that data-centric explanations [7] have the potential to assist users in assessing fairness.”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [7]
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Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
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Metadata
- Authors
- Anik, A. I., & Bunt, A.
- Affiliations
- University of Manitoba (CA)
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-13
- OpenAlex
- W3160343688
- Cited by
- 103
- References
- 99
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 29 · 2023: 30 · 2022: 12 · 2021: 3
- FWCI
- 8.55
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Mspace (University of Manitoba) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- training data transparency, explainable machine learning, algorithmic fairness, training data bias, stakeholder communication, formative research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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