This ground truth is muddy anyway”
Ground Truth Data Assemblages for Medical AI Development
Bibliographic Data
| ID | 10756752 |
|---|---|
| Authors | Charlotte Högberg (0000-0002-1462-6325, Lund University, corresponding author) |
| Year | 2025 |
| Volume | 62 |
| Issue | 1-2 |
| Pages | 85-106 |
| Publication date | 2025-06-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociologisk Forskning (JOURNAL) |
| Journal identifiers | ISSN: 0038-0342 • E-ISSN: 2002-066X |
| Publisher | Sociologisk Forskning, Swedish Sociological Association (PUBLISHER) |
| DOI | 10.37062/sf.62.27826 |
| OpenAlex | W4411243923 |
| Language | EN |
| Citations received | 2 |
| References cited | 11 |
This article explores assemblages of ground truth datasets for the development of medical artificial intelligence (AI). By drawing from interviews and observations, I examine how AI experts developing medical AI relate to the referential truth basis of their work, their ground truths, as an epistemic concern. By addressing how datasets are assembled from different sources, and produced, augmented and synthesised, this study shows how ground truths are valued based on humanness, quality of medical expert judgements, temporality and technical qualities. Moreover, this article analyses truth practices as productive moments in AI development, the role of human expertise and the perceived strengths and limits of expert-based annotations. The valuations of ground truths shatter the image of medical classifications, and AI models, as stable neutral entities. Moreover, this article shows how valuations of ground truths encompass more than alignment with standardised expertise. To better understand the possibilities for medical AI to live up to ideals of accuracy, fairness, trustworthiness and transparency, we need moreknowledge on assumptions, negotiations and epistemic concerns upon which medical AI is built
Common ground · Epistemology · Ground truth · Sociology · Artificial Intelligence · Artificial Intelligence in Healthcare and Education · Communication · Computer Science · Ethics and Social Impacts of AI · Philosophy · Race, Genetics, and Society
Building truths in AI
The Expert Interview and Changes in Knowledge Production
Groundwork for AI
We get the algorithms of our ground truths
The Social Construction of Facts and Artefacts
Standardization in Action
The ethics and politics of data sets in the age of machine learning
Thinking critically about and researching algorithms
The Truthiness about Hurricane Catastrophe Models
How should we theorize algorithms? Five ideal types in analyzing algorithmic normativities
Algorithms as culture
| Citations per year | 2 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |