Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI

Abstract

eXplainable AI focuses on generating explanations for the output of an AI algorithm to a user, usually a decision-maker. Such user needs to interpret the AI system in order to decide whether to trust the machine outcome. When addressing this challenge, therefore, proper attention should be given to produce explanations that are interpretable by the target community of users.In this chapter, we claim for the need to better investigate what constitutes a human explanation, i.e. a justification of the machine behaviour that is interpretable and actionable by the human decision makers. In particular, we focus on the contributions that Human Intelligence can bring to eXplainable AI, especially in conjunction with the exploitation of Knowledge Graphs.Indeed, we call for a better interplay between Knowledge Representation and Reasoning, Social Sciences, Human Computation and Human-Machine Cooperation research -as already explored in other AI branches -in order to support the goal of eXplainable AI with the adoption of a Human-in-the-Loop approach.

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“…However, it is important to note that XAI methods are not without challenges. The interpretability of AI models and the explanations provided by XAI techniques can be subjective and contextdependent 48 . Different stakeholders may have different requirements and interpretations of what constitutes a satisfactory explanation 48 .…”

“…The interpretability of AI models and the explanations provided by XAI techniques can be subjective and contextdependent 48 . Different stakeholders may have different requirements and interpretations of what constitutes a satisfactory explanation 48 . Additionally, the trade-off between accuracy and interpretability should be carefully considered, as more interpretable models may sacrifice some level of predictive performance 49 .…”