ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations
This addresses the problem of interpretability for users of black-box ML models, but it appears incremental as it combines existing explanation types into a unified framework.
The paper tackles the need to explain complex machine learning models by proposing ASTERYX, a model-agnostic approach that generates both symbolic explanations (e.g., sufficient reasons and counterfactuals) and score-based ones, with experimental results demonstrating its feasibility and effectiveness.
The ever increasing complexity of machine learning techniques used more and more in practice, gives rise to the need to explain the predictions and decisions of these models, often used as black-boxes. Explainable AI approaches are either numerical feature-based aiming to quantify the contribution of each feature in a prediction or symbolic providing certain forms of symbolic explanations such as counterfactuals. This paper proposes a generic agnostic approach named ASTERYX allowing to generate both symbolic explanations and score-based ones. Our approach is declarative and it is based on the encoding of the model to be explained in an equivalent symbolic representation, this latter serves to generate in particular two types of symbolic explanations which are sufficient reasons and counterfactuals. We then associate scores reflecting the relevance of the explanations and the features w.r.t to some properties. Our experimental results show the feasibility of the proposed approach and its effectiveness in providing symbolic and score-based explanations.