Improving Labeling Through Social Science Insights: Results and Research Agenda. - Jacob Beck; Stephanie Eckman; Robert F. Chew; Frauke Kreuter (HCI (43), 2022)

Annotation Sensitivity: Training Data Collection Methods Affect Model Performance. - Christoph Kern 001; Stephanie Eckman; Jacob Beck; Rob Chew 001; Bolei Ma; Frauke Kreuter (EMNLP (Findings), 2023)

Position: Insights from Survey Methodology can Improve Training Data. - Stephanie Eckman; Barbara Plank; Frauke Kreuter (ICML, 2024)

The Science of Data Collection: Insights from Surveys can Improve Machine Learning Models. - Stephanie Eckman; Barbara Plank; Frauke Kreuter (CoRR, 2024)

Mitigating Selection Bias with Node Pruning and Auxiliary Options. - Hyeong Kyu Choi; Weijie Xu; Chi Xue; Stephanie Eckman; Chandan K. Reddy (ACL (1), 2025)

Correcting Annotator Bias in Training Data: Population-Aligned Instance Replication (PAIR). - Stephanie Eckman; Bolei Ma; Christoph Kern 001; Robert F. Chew; Barbara Plank; Frauke Kreuter (CoRR, 2025)

SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions. - Weijie Xu; Shixian Cui; Xi Fang; Chi Xue; Stephanie Eckman; Chandan K. Reddy (CoRR, 2025)