CONCEPT
Big-Data Intimidation
Klein's diagnostic for the rhetorical use of impressive variable counts to create confidence the underlying analysis does not support.
Big-data intimidation is the third of Klein's three diagnostic categories for identifying methodologically weak claims of AI superiority over human experts, alongside smuggled expertise and learning confounds. The concept operates at the rhetorical rather than the methodological level: impressive variable counts are cited to create an aura of comprehensiveness that the actual predictive architecture does not support. Klein illustrated the concept through an emergency department prediction study that cited over sixteen thousand variables — but whose empirical optimum turned out to require only two hundred twenty-four variables, with performance plateauing at approximately twenty. The gap between the number used and the number needed reveals the rhetorical device: large numbers deployed to improve audience confidence rather than predictive accuracy.
In The You On AI Field Guide
The concept has significance beyond individual studies. It identifies a structural feature of how AI systems are presented to non-technical audiences, including executives, policymakers, and practitioners whose decisions about AI deployment will shape the technology's impact across organizations and domains. The rhetorical deployment of scale — millions of training