Automation plays an increasingly important role in science, but the social sciences have been comparatively slow to take advantage of emerging technologies and methods. In this review, we argue that greater investment in automation would be one of the most effective and cost-effective ways to boost the reliability, validity, and utility of social science research. We identify five core areas ripe for potentially transformative investment, including (1) machine-readable standards, (2) data access platforms, (3) search and discoverability, (4) claim validation, and (5) insight generation. In each case, we review limitations associated with current practices, identify concrete opportunities for improvement via automation, and discuss near-term barriers to progress. We conclude with a discussion of practical and ethical considerations researchers will need to keep in mind when working to enhance and accelerate social science progress via automation.
To continue reading this piece on the Harvard Data Science Review, by Tal Yarkoni, Dean Eckles, James A. J. Heathers, Margaret C. Levenstein, Paul E. Smaldino, and Julia Lane, click here.