Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection? An experimental study with a classification algorithm

Advances in AI and our limited human capabilities have made AI decision-making opaque to humans. One prerequisite for enhancing the transparency of AI recommendations is improving AI explainability as humans need to be enabled to take responsibility for their actions even with AI support. Our study aims to tackle this issue by investigating two basic approaches to explainability: We evaluate numerical explanations, such as certainty measures, against verbal explanations, such as those provided by LLM as explanatory agents. Specifically, we examine whether verbal or numerical (or both) explanations in tasks of high uncertainty lure users into false beliefs or, on the contrary, promote appropriate reliance. Drawing on an experiment with 441 participants, we explore the dynamics of non-expert users' interactions with AI under varying explanatory conditions. Results show that explanations significantly improve reliance and decision accuracy. Numerical explanations aid in identifying uncertainties and errors, but the users' reliance on the advice falls far behind the given numerical certainty. Verbal explanations foster higher reliance while increasing the risk of over-reliance. Combining both explanation types enhances reliance but further amplifies blind trust in AI.