Michael Lenke
ABSTRACT
Most explainable AI (XAI) approaches focus on mathematically correct and complete explanations. In contrast to that, everyday explanations are social practices that are influenced by many factors, e.g. the social and institutional context of the explanation. Everyday explanations are therefore co-constructed by both the explainer and the explainee. Assuming that everyday co-constructed explanations are easy to understand for citizens, they can help to make AI understandable for people who do not have affinity for technology. In this context, concrete and simplified examples that are adapted to the needs of the learner can reduce the complexity of the explanation furthermore.
This Ph.D. project investigates the effects of co-constructed explanations created while exploring notional machines on the mental models of AI of everyday people. The goal is to identify aspects of AI technology which are relevant for everyone, to empower people to become a responsible and active members of society in a digitalized world where AI technology is ubiqitious.
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