Human Control Redressed: Comparing AI-To-Human Vs. Human-To-Human Predictability in a Real-Effort Task

Abstract

Predictability is a prerequisite for effective human control of artificial intelligence (AI). The inability to predict malfunctioning of AI, for example, impedes timely human intervention. In this paper, we empirically investigate how AI’s predictability compares to the predictability of humans in a real-effort task. We show that humans are worse at predicting AI performance than at predicting human performance. Importantly, participants are not aware of the differences in relative predictability of AI and overestimate their prediction skills. These results raise doubts about the human ability to effectively exercise control of AI — at least in certain contexts.

Keywords: Human Control; AI Predictability; Lunar Lander Game; Human-Computer Interaction