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The shift from symbolic AI systems to black-box, sub-symbolic, andstatistical ones has motivated a rapid increase in the interest towardexplainable AI (XAI), i.e. approaches to make black-box AI systems explainableto human decision makers with the aim of making these systems more acceptableand more usable tools and supports. However, we make the point that, ratherthan always making black boxes transparent, these approaches are at risk of\emph{painting the black boxes white}, thus failing to provide a level oftransparency that would increase the system's usability and comprehensibility;or, even, at risk of generating new errors, in what we termed the\emph{white-box paradox}. To address these usability-related issues, in thiswork we focus on the cognitive dimension of users' perception of explanationsand XAI systems. To this aim, we designed and conducted a questionnaire-basedexperiment by which we involved 44 cardiology residents and specialists in anAI-supported ECG reading task. In doing so, we investigated different researchquestions concerning the relationship between users' characteristics (e.g.expertise) and their perception of AI and XAI systems, including their trust,the perceived explanations' quality and their tendency to defer the decisionprocess to automation (i.e. technology dominance), as well as the mutualrelationships among these different dimensions. Our findings provide acontribution to the evaluation of AI-based support systems from a Human-AIinteraction-oriented perspective and lay the ground for further investigationof XAI and its effects on decision making and user experience.

Painting the black box white: experimental findings from applying XAI to an ECG reading setting