(Human) Attention Is (Still) All You Need: Human oversight makes AI-assisted social science reliable
arXiv:2606.12848v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for tasks once reserved for trained researchers, including hypothesis generation, specification choice, and drafting conclusions. We argue that the reliability of AI-assisted research depends not only on model capability, but also on how cognitive labour is structured between humans and machines. We study this problem through Human-in-the-Loop Economic Research (HLER), a decision architecture based on pre-commitment, decision sequencing, accountability, and attention allocation. In a pre-specifie
The rapid deployment of large language models across various analytical tasks necessitates a re-evaluation of research methodologies and the allocation of cognitive labor.
This research provides a framework for integrating AI into high-stakes analytical work reliably, which is critical for maintaining confidence in AI-assisted outputs and maximizing human productivity.
The explicit structuring of human-machine interaction in research, emphasizing pre-commitment and accountability, shifts from ad-hoc AI use to systematic integration.
- · Social science researchers
- · AI ethics and governance frameworks
- · Organizations deploying AI for critical analysis
- · Unstructured AI-assisted research
- · AI tools lacking explainability
- · Researchers resistant to human-in-the-loop models
Increased adoption of structured human-in-the-loop AI methodologies in academic and corporate research environments.
Development of specialized AI tools and platforms designed specifically for 'Human-in-the-Loop Economic Research' and similar structured collaboration.
Re-definition of 'expertise' in fields heavily impacted by AI, emphasizing human oversight and critical judgment over pure data processing.
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Read at arXiv cs.AI