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This paper is devoted to the use of the GAI (Generalized Additive Independence) model in a MultiCriteria Decision Making context. We first discuss on some new conditions (concerning the sign and monotonicity) to add on the terms appearing in a GAI model. Secondly, we propose some algorithms to propose the learning examples to change or remove, together with an explanation of this, when there are inconsistencies in the learning data. Finally, we propose some importance and interaction indices to interpret a GAI model.

Use of the GAI model in multi-criteria decision making: inconsistency handling, interpretation