Recognitive Inquiry, in Plain Language

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A three-page plain-language companion to the preprint Recognitive Inquiry: Human–AI Participation, Decisions, and Possible Futures (doi:10.5281/zenodo.22851498). It explains the paper's central ideas without the academic vocabulary: what recognition and recognizability mean, why the quality of human–AI participation matters before a decision is made, how the proposition could be tested, and what changes for people who design and use AI systems. The full argument, evidence and references are in the paper. The short versionAdding AI to human judgment does not reliably produce better decisions, and part of the reason may sitearlier than the decision itself. A review of 106 experiments found that human-AI teams beat humansworking alone on average, yet fell short of the better of human or AI on its own. Teams did worst ondecision tasks and fared better on content creation.Recognitive Inquiry (RI) studies the stretch before a decision, when a situation is still taking shape.Which details stand out, how the problem gets described, and which questions get asked start to formbefore anyone compares options. RI asks how the way people and AI work together shapes what comesinto view, and how what comes into view shapes the next round of work.