CONCEPT
Abductive Doubles
The systematic production, in AI-assisted inquiry, of outputs that exhibit the surface characteristics of abductive inference without its logical substance — three varieties that Peirce's framework distinguishes.
An abductive double is an output that looks like the product of genuine discovery but fails one of the three logical requirements of abduction. The Peirce volume identifies three varieties: the unmotivated hypothesis (a clever connection that responds to the prompt rather than to a genuine anomaly), the overdetermined hypothesis (a suggestion so well-supported by training data that it carries no genuine explanatory risk), and the simulated surprise (meta-level astonishment at the machine's capability mistaken for object-level encounter with anomaly). Each variety presents a distinct risk, and each is diagnostically identifiable through careful attention to whether the surprise is genuine, whether the hypothesis is responsive, and whether the inference carries real risk.
In The You On AI Field Guide
The unmotivated hypothesis is the most common. The AI generates a connection — a structural analogy, an unexpected example, a reframing — that is clever and well-articulated. But it does not respond to a genuine anomaly in the inquiry. It responds to the prompt. The human asked