Improving Robustness Against Bias in Social Science Machine Learning: The Promise of Instruction-Based Models

Practical Solutions and Value

Language models (LMs) in computational text analysis offer enhanced accuracy and versatility, but ensuring measurement validity remains a critical challenge. Researchers from Communication Science, Vrije Universiteit Amsterdam and Department of Politics, IR and Philosophy, Royal Holloway University of London propose instruction-based models as a potential solution.

The study investigates the impact of group-based biases in machine learning training data on measurement validity across various classifiers, datasets, and social groups. The findings suggest that instruction-based models like BERT-NLI may offer improved measurement validity in supervised machine learning for social science tasks.

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