Enhancing the Intelligibility of Boolean Decision Trees with Concise and Reliable Probabilistic Explanations

Overview & Synopsis

This work deals with explainable artificial intelligence (XAI),specifically focusing on improving the intelligibility of decision trees throughreliable and concise probabilistic explanations. Decision trees are popular because they are considered highly interpretable. Due to cognitivelimitations, abductive explanations can be too large to be interpretableby human users. When this happens, decision trees are far from beingeasily interpretable. In this context, our goal is to enhance the intelligibility of decision trees by using probabilistic explanations. Drawinginspiration from previous work on approximating probabilistic explanations, we propose a greedy algorithm that enables us to derive conciseand reliable probabilistic explanations for decision trees. We provide a detailed description of this algorithm and compare it to the state-of-the-artSAT encoding, emphasizing the gains in intelligibility and highlightingits empirical effectiveness.