Some decision problems can be formulated as sorting models which consist in assigning alternatives evaluated on several criteria to ordered categories. The implementation of a multiple criteria sorting model requires to set the values of the preference parameters used in the model. Rather thanfixing directly the values of these parameters, an usual approach is to infer these values from assignment examples provided by the decision maker (DM), i.e., alternatives for which he/she specifies a required category or interval of acceptable categories. However, the judgments expressed by DMs through assignment examples can be inconsistent, i.e., may not match the sorting model. In such situations, it is necessary to support the DMs in the resolution of this inconsistency. In this paper, we propose algorithms that calculate different ways to modify the set of assignment examples so that the information can be represented in the sorting model. The modifications considered are either the removal of examples or the relaxation of existing assignments. These algorithms incorporate information about the confidence attached by the DMs to each assignment example. These algorithms aim at finding and ranking the solutions to solve inconsistency that the DMs are most likely to accept.