arXiv:2605.21743v1 Announce Type: new
Abstract: A growing literature uses artificial intelligence platform conversation logs to measure occupation exposure. We show that these scores partly measure platform user base rather than the workforce. Holding outcome, sample, controls, and estimator fixed while varying only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9, and within-vendor consumer-versus-enterprise channels produce estimates that disagree in sign. Reweighting to Bureau of Labor Statistics workforce shares attenuates estimates by 42 to 93 percent. We formalize the non-classical measurement error, derive probability limits and partial-identification bounds for employment elasticities. The bias understates substitution more than augmentation.
Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection
arXiv:2605.27155v1 Announce Type: cross Abstract: Testing object detectors in safety-critical domains requires semantically meaningful probes beyond pixel-level corruptions. We present SemProbe, a tool for semantic

