The article discusses how artificial intelligence is reshaping measurement in economics by converting unstructured data into structured variables at low cost, enabling large‑scale measurement that was previously infeasible. It outlines three stages—discovery, construct definition, and observation—where AI impacts the measurement pipeline and stresses the importance of rigorous validation to ensure credible inference. The review offers guidance on navigating the shift from a single scalable measure to multiple plausible ones that can lead to differing empirical conclusions.
arXiv:2606. 11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions.
The paper introduces a general-purpose framework that automatically discovers insights from unstructured data, such as text from open-ended surveys. It transforms these data into high-dimensional, structured datasets of interpretable concept measurements, defines concept-level parameters and null hypotheses, and applies validated high-dimensional multiple testing algorithms to select discoveries. The framework also generates and evaluates human-interpretable natural language descriptions of the findings, enabling fast, inexpensive sensitivity analysis and replication.
arXiv:2606. 15708v1 Announce Type: new Abstract: Welcome to the ninth edition of the AI Index report.
arXiv:2605. 02640v2 Announce Type: replace Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge.
The paper introduces a framework for hypothesis testing that combines inexpensive AI judgments with selective human verification to control type‑I and type‑II errors while minimizing cost. It derives an information‑theoretic lower bound on the minimum cost and proposes the SCALE policy, a sequential, cost‑aware strategy that adapts AI scoring and human escalation. SCALE is proven valid for finite samples and asymptotically matches the lower bound, achieving significant savings when both AI and human inputs are valuable.
arXiv:2607. 26064v1 Announce Type: cross Abstract: AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversight.
The paper "AI in Science: Early Insights" analyzes AI’s impact on scientific work using data from 15 million Gemini interactions, 2,600 specialized AI models, and a survey of 600 scientists. It finds widespread AI adoption, complementary use of large language models and specialized tools, significant productivity gains of about seven hours per week, and a shift in research bottlenecks toward hypothesis backlog and verification needs. The study suggests AI can boost scientific productivity but its full effect depends on addressing new downstream challenges.
arXiv:2606. 18874v1 Announce Type: new Abstract: AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claims often remains implicit inside model inference.
The paper examines how generative AI (GenAI) changes the way financial analysts produce information, using the 2023 integration of GenAI into FACTSET as a natural experiment. It finds that FACTSET reports become richer—adding 26% more distinct sources, 24% broader topics, and 21% more analytical methods—while also being more timely. However, the increased information load leads to lower forecast accuracy for analysts, suggesting a human attention bottleneck, whereas a machine-learning benchmark does not suffer the same decline.
arXiv:2607. 10039v1 Announce Type: cross Abstract: Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing.
arXiv:2606. 31273v1 Announce Type: new Abstract: AI-assisted research has entered a stage in which the central question is not only whether systems can generate hypotheses, run experiments, or produce manuscripts, but whether their scientific claims are calibrated to the evidence that supports them.
The paper introduces a hypothesis testing framework for adaptive auditing of AI systems, addressing the challenge of limited, opportunistically collected data. It defines two competing null hypotheses—one from the model’s perspective and one from the auditor’s—and uses Safe Anytime-Valid Inference (SAVI) to perform betting-based tests that yield simultaneous e-processes. The authors prove that, with a powerful auditor, the two hypotheses become asymptotically inverse, allowing a global robustness certification, and demonstrate empirically that their methods control Type‑I error and can reach conclusions with as few as 20 observations.