# The Measurement Revolution? Credible Measurement and Inference in the Age of AI

arXiv: https://arxiv.org/abs/2608.23524 · HTML: https://mlsanity.com/paper/2608.23524/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation](https://mlsanity.com/paper/2509.08825/) (2025)
- [HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation](https://mlsanity.com/paper/2504.11524/) (2025)
- [Large Language Models: An Applied Econometric Framework](https://mlsanity.com/paper/2412.07031/) (2024)
- [What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data](https://mlsanity.com/paper/2510.26202/) (2025)
- [Deep Learning for Economists](https://mlsanity.com/paper/2407.15339/) (2024)
- [In your own words: computationally identifying interpretable themes in free-text survey data](https://mlsanity.com/paper/2603.26930/) (2026)

## Built on by (most cited first, 3 of 3)

- [EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues](https://mlsanity.com/paper/2609.27043/) (2026)
- [Reliable but Design-Sensitive: Instrument Uncertainty in LLM Annotation](https://mlsanity.com/paper/2609.35824/) (2026)
- [Active Learning for Finding a Needle in a Haystack](https://mlsanity.com/paper/2610.15165/) (2026)

## Abstract (arXiv)

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.