Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

Although future regulations increasingly advocate that AI applications must be interpretable by users, we know little about how such explainability can affect human information processing. By conducting two experimental studies, we help to fill this gap. We show that explanations pave the way for AI systems to reshape users' understanding of the world around them. Specifically, state-of-the-art explainability methods evoke mental model adjustments that are subject to confirmation bias, allowing misconceptions and mental errors to persist and even accumulate. Moreover, mental model adjustments create spillover effects that alter users' behavior in related but distinct domains where they do not have access to an…

• VF
  Volkswagen Foundation