Improving Robustness Against Bias In Social Science Machine Learning: The Promise Of Instruction-based Models

Researchers address the issue of measurement validity in supervised machine learning for social science tasks, particularly focusing on the impact of biases in fine-tuning data. They aim to bridge the gap in social science literature by investigating the extent of bias impact, the robustness of different machine learning approaches, and the potential of meaningful interpretation of results.