Deception Analysis with Artificial Intelligence: An Interdisciplinary Perspective

TL;DR

This paper presents an interdisciplinary approach to understanding AI deception, emphasizing the need for a computational theory and proposing the DAMAS framework for socio-cognitive modeling of deception in hybrid societies.

Contribution

It introduces the DAMAS framework for modeling deception in multi-agent systems, integrating insights from multiple disciplines to address AI deception.

Findings

Proposes the DAMAS framework for deception analysis

Highlights the importance of interdisciplinary perspectives

Emphasizes the need for a computational theory of deception

Abstract

Humans and machines interact more frequently than ever and our societies are becoming increasingly hybrid. A consequence of this hybridisation is the degradation of societal trust due to the prevalence of AI-enabled deception. Yet, despite our understanding of the role of trust in AI in the recent years, we still do not have a computational theory to be able to fully understand and explain the role deception plays in this context. This is a problem because while our ability to explain deception in hybrid societies is delayed, the design of AI agents may keep advancing towards fully autonomous deceptive machines, which would pose new challenges to dealing with deception. In this paper we build a timely and meaningful interdisciplinary perspective on deceptive AI and reinforce a 20 year old socio-cognitive perspective on trust and deception, by proposing the development of DAMAS -- a holistic Multi-Agent Systems (MAS) framework for the socio-cognitive modelling and analysis of deception. In a nutshell this paper covers the topic of modelling and explaining deception using AI approaches from the perspectives of Computer Science, Philosophy, Psychology, Ethics, and Intelligence Analysis.