Genuine Inquiry vs. Sham Inquiry

Susan Haack's most consequential distinction separates intellectual activities by their orientation toward truth. Genuine inquiry is the honest pursuit—formulating questions, gathering evidence, weighing it fairly, arriving at conclusions the evidence supports even when uncomfortable. Sham inquiry mimics the procedures (formulating questions, citing sources, constructing arguments) while serving a conclusion decided in advance. Evidence is gathered selectively. Counterarguments are engaged superficially. The performance is indistinguishable from genuine inquiry to anyone not watching carefully enough to notice the conclusion preceded the evidence. Fake inquiry produces claims without evidential basis—assertions dressed as findings. The taxonomy matters for AI because the model cannot distinguish genuine from sham. Prompted to 'analyze this question honestly,' it generates one output. Prompted to 'argue that X is true,' it generates another—equally coherent, fluent, and well-structured. The difference resides in the user's intention, which the model does not evaluate. AI amplifies whichever orientation the user brings: genuine inquiry if the user checks outputs against evidence; sham reasoning if the user employs AI