Ground truth datasets are essential for training and evaluating machine learning models, as stated in a position paper by Charlotte Högberg and colleagues. The paper, submitted on May 28, 2026, argues that these datasets are not unbiased metrics but are shaped by human and technological influences. This perspective is crucial for improving the reliability of machine learning applications.
Understanding Ground Truths in Machine Learning
The authors emphasize that ground truths should not be viewed as universal truths. Instead, they are contingent on specific contexts and the choices made during their creation. This understanding allows for a more nuanced approach to data interpretation and usage, which is vital for the machine learning community.
By recognizing the constructed nature of these datasets, practitioners can better articulate the strengths and limitations of their models. This can lead to enhanced transparency and accountability within the field.
The Importance of Situated Reliability
In the paper, the authors propose the concept of situated reliability, which focuses on the context-dependent nature of ground truths. By acknowledging the circumstances under which datasets are created, machine learning practitioners can improve model performance and applicability.
