  Artificial Intelligence (AI) systems have been increasingly used to make
decision-making processes faster, more accurate, and more efficient. However,
such systems are also at constant risk of being attacked. While the majority of
attacks targeting AI-based applications aim to manipulate classifiers or
training data and alter the output of an AI model, recently proposed Sponge
Attacks against AI models aim to impede the classifier's execution by consuming
substantial resources. In this work, we propose \textit{Dual Denial of Decision
(DDoD) attacks against collaborative Human-AI teams}. We discuss how such
attacks aim to deplete \textit{both computational and human} resources, and
significantly impair decision-making capabilities. We describe DDoD on human
and computational resources and present potential risk scenarios in a series of
exemplary domains.
