François Leborgne

The surge of Artificial Intelligence (AI) for automatic decision-making raises concerns about transparency and interpretability of AI models. Explainable AI (XAI) addresses this by providing insights into AI predictions. Despite the availability of various methods for explaining decisions based on tabular data, there is no consensus on their effectiveness for different types of users. This paper introduces a novel XAI method, the Visual Map, and presents a human-grounded evaluation study comparing it with three common XAI methods. In an online experiment (N = 49), participants with either high or low AI-literacy evaluated all four methods in terms of explanation satisfaction, cognitive load, and overall evaluation in the same classification task environment. High AI-literacy participants were largely indifferent to the four methods, whereas low AI-literacy participants favoured the visual map, perceiving it as the least cognitively demanding. Our findings contribute to the evaluation and development of XAI methods for different types of end-users.