We Need Improved Data Curation and Attribution in AI for Scientific Discovery
This addresses data integrity issues in AI for scientific discovery, though it is incremental by building on existing synthetic data detection efforts.
The paper examines challenges in distinguishing synthetic from real experimental data for scientific discovery, finding that 75% of experimental datasets have low adoption rates and proposes watermarking real data to improve traceability, estimating that watermarking less than half of real data annually could sustain model robustness.
As the interplay between human-generated and synthetic data evolves, new challenges arise in scientific discovery concerning the integrity of the data and the stability of the models. In this work, we examine the role of synthetic data as opposed to that of real experimental data for scientific research. Our analyses indicate that nearly three-quarters of experimental datasets available on open-access platforms have relatively low adoption rates, opening new opportunities to enhance their discoverability and usability by automated methods. Additionally, we observe an increasing difficulty in distinguishing synthetic from real experimental data. We propose supplementing ongoing efforts in automating synthetic data detection by increasing the focus on watermarking real experimental data, thereby strengthening data traceability and integrity. Our estimates suggest that watermarking even less than half of the real world data generated annually could help sustain model robustness, while promoting a balanced integration of synthetic and human-generated content.