How to Manage Output Uncertainty: Targeting the Actual End User Problem in Interactions with AI

Zelun Tony Zhang and Heinrich Hußmann

Joint Proceedings of the ACM IUI 2021 Workshops,

July 2021


Given the opaqueness and complexity of modern AI algorithms, there is currently a strong focus on developing transparent and explainable AI, especially in high-stakes domains. We claim that opaqueness and complexity are not the core issues for end users when interacting with AI. Instead, we propose that the output uncertainty inherent to AI systems is the actual problem, with opaqueness and complexity as contributing factors. Transparency and explainability should therefore not be the end goals, as such a focus tends to place the human into a passive supervisory role in what is in reality an algorithm-centered system design. To enable effective management of output uncertainty, we believe it is necessary to focus on truly human-centered AI designs that keep the human in an active role of control. We discuss the conceptual implications of such a shift in focus and give examples from literature to illustrate the more holistic, interactive designs that we envision.