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The ever increasing complexity of machine learning techniques used more andmore in practice, gives rise to the need to explain the predictions anddecisions of these models, often used as black-boxes. Explainable AI approachesare either numerical feature-based aiming to quantify the contribution of eachfeature in a prediction or symbolic providing certain forms of symbolicexplanations such as counterfactuals. This paper proposes a generic agnosticapproach named ASTERYX allowing to generate both symbolic explanations andscore-based ones. Our approach is declarative and it is based on the encodingof the model to be explained in an equivalent symbolic representation, thislatter serves to generate in particular two types of symbolic explanationswhich are sufficient reasons and counterfactuals. We then associate scoresreflecting the relevance of the explanations and the features w.r.t to someproperties. Our experimental results show the feasibility of the proposedapproach and its effectiveness in providing symbolic and score-basedexplanations.

ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations