DDoD: Dual Denial of Decision Attacks on Human-AI Teams

• Published in: IEEE pervasive computing Vol. 22; no. 1; pp. 1 - 8
• Main Authors: Tag, Benjamin, van Berkel, Niels, Verma, Sunny, Zhao, Benjamin Zi Hao, Berkovsky, Shlomo, Kaafar, Dali, Kostakos, Vassilis, Ohrimenko, Olga
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Classifiers; Collaboration; Data models; Decision making; Predictive models; Task analysis; Teams; Training; Training data; Uncertainty
• ISSN: 1536-1268; 1558-2590
• DOI: 10.1109/MPRV.2022.3218773

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 dual denial of decision (DDoD) attacks against collaborative human-AI teams. We discuss how such attacks aim to deplete 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.