aiqbench analysis
This review addresses the critical shift in empirical economics where AI lowers the cost of data extraction but complicates variable selection, potentially leading to divergent empirical conclusions. The authors argue that credible inference now demands rigorous validation protocols anchored to explicit criteria rather than informal proxy assumptions, even when dealing with arbitrarily biased predictions. For researchers integrating unstructured data into models, this work outlines essential frameworks for maintaining statistical integrity in the age of AI-generated variables.
The Measurement Revolution? Credible Measurement and Inference in the Age of AI
arXiv:2608.23524v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline---discovery, construct definition, and observation---and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable.
Title:The Measurement Revolution? Credible Measurement and Inference in the Age of AI
Abstract:Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline---discovery, construct definition, and observation---and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable.
This review accompanies the National Bureau of Economic Research 2026 Methods Lecture on "Estimation and Inference with AI-Generated Data."
General Economics (econ.GN); Artificial Intelligence (cs.AI); Applications (stat.AP)
arXiv:2608.23524 [econ.GN]
(or arXiv:2608.23524v1 [econ.GN] for this version)
https://doi.org/10.48550/arXiv.2608.23524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 267 words
Related coverage
Problems of Proliferation
LessWrong · Sep 29, 2026
How Musicians Can Get Paid for Training AI
IEEE Spectrum · Jun 17, 2026
Wall Street Week Ahead: Rates, Oil, AI, Quantum, Defense, Metals and Bitcoin
livetradingnews.com · Sep 21, 2026
Would We See It Coming? Preference Falsification Cascades in Multi-Agent Systems
LessWrong · Aug 4, 2026