Abstract: We present a probabilistic framework to represent and quantify model-form uncertainties in the reduced-order modeling of complex systems using operator inference techniques. Such uncertainties can arise in the selection of an appropriate state and can dramatically impact the robustness of the predictions. The proposed method captures these uncertainties by expanding the approximation space through the randomization of the projection matrix. The efficacy of the approach is assessed on canonical problems in fluid mechanics.