AI-Ready Research Workflows in Computational Social Science: Lessons on Building a Shared Language for Interdisciplinary Collaboration

The integration of Artificial Intelligence (AI) into social sciences and humanities (SSH) research faces significant hurdles, including technical complexities, validation process lags, and reproducibility challenges. A reported two-year initiative successfully developed a research workflow to enable an academic unit to leverage a supercomputer for querying, analyzing, and enriching a large scholarly database, demonstrating a potential pathway to overcome these barriers through structured operational approaches.

Why it matters

Addressing the technical and methodological gaps in AI adoption within research domains like SSH is crucial for leveraging advanced computational capabilities and ensuring the rigor and reproducibility of scientific inquiry. Establishing robust, AI-ready research workflows can unlock new avenues for data analysis and discovery, enhancing the capacity of institutions to generate impactful knowledge.

Key insights

• AI adoption in social sciences and humanities (SSH) is hindered by technical barriers related to high-performance computing (HPC).
• Validation processes for AI applications in SSH are struggling to keep pace with rapid AI advancements.
• Existing reproducibility standards in SSH are often not met by current AI-driven research teams.
• Research workflows, common in life sciences, offer a solution by encoding and abstracting technical complexity into repeatable routines.
• There is a scarcity of documented methodologies for building such research workflows specifically within SSH.
• A two-year project successfully implemented an AI-ready research workflow, enabling a Science and Technology Studies unit to utilize a supercomputer for analyzing a large scholarly database (OpenAlex).

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.24914