Designing Ground Truth and the Social Life of Labels
Abstract
Ground-truth labeling is an important activity in machine learning. Many studies have examined how crowdworkers apply labels to records in machine learning datasets. However, there have been few studies that have examined the work of domain experts when their knowledge and expertise are needed to apply labels.
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Prior research has shown that so-called 'ground truth' labels are shaped by specific contexts, such as annotators' backgrounds and expertise, as well as broader organizational control.
“Prior research has shown that so-called 'ground truth' labels are shaped by specific contexts, such as annotators' backgrounds and expertise, as well as broader organizational control [1, 76].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [76]
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References 121 · Cited by 83
- Authors
- Muller, M., Wolf, C. T., Andres, J., Desmond, M., Joshi, N. N., Ashktorab, Z., Sharma, A., Brimijoin, K., Pan, Q., Duesterwald, E., & Dugan, C.
- Affiliations
- IBM (United States) (US)
- Independent Consultant, United States
- IBM Research Australia, Australia
- ORCID
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-16
- OpenAlex
- W3163469193
- Cited by
- 83
- References
- 121
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 22 · 2023: 17 · 2022: 16 · 2021: 2
- FWCI
- 6.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- ground truth annotation, domain experts, expert labeling, crowd workers, label quality
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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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