3 papers
stat.ME2026
From Ground Truth to Measurement: A Statistical Framework for Human Labeling
Robert Chew, Stephanie Eckman, Christoph Kern +1
Supervised machine learning assumes that labeled data provide accurate measurements of the concepts models are meant to learn. Yet in practice, human labeling introduces systematic…
cs.HC2025
Bias in the Loop: How Humans Evaluate AI-Generated Suggestions
Jacob Beck, Stephanie Eckman, Christoph Kern +1
Human-AI collaboration increasingly drives decision-making across industries, from medical diagnosis to content moderation. While AI systems promise efficiency gains by providing a…
stat.ME2025
Aligning NLP Models with Target Population Perspectives using PAIR: Population-Aligned Instance Replication
Stephanie Eckman, Bolei Ma, Christoph Kern +3
Models trained on crowdsourced annotations may not reflect population views, if those who work as annotators do not represent the broader population. In this paper, we propose PAIR…