Deception Analysis with Artificial Intelligence: An Interdisciplinary Perspective

History, Economics, Politics, Philosophy, Communication Sciences, Sociology, and the Cognitive Sciences have looked at deception from perspectives that are predominantly anthropocentric. Thus, the significant knowledge we have about deception revolves around its human nature. This acquired knowledge emphasises that deception plays an important role for humans and that deception is a multi-layered phenomenon which takes numerous forms during social interactions. However, more recently, the anthropocentric grip on understanding deception has weakened. Research on deception (and its detection) is expanding beyond human agents, to deceptive technologies, due to the current hybridisation of our societies. Hybrid societies are'self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical)' [Hamann et al., 2016]. Nowadays, AI technologies play a crucial role in hybrid societies, but research in AI and deception has not progressed enough to allow us to understand and predict how advancements in the design of AI agents will impact hybrid societies. A particular threat to the hybridisation of societies is the development of fully autonomous deceptive AI agents that will be able to form their own reasons and methods to perform deception, as well as out-think and outsmart humans and other AI agents [Sarkadi, 2021]. By fully autonomous deceptive AI agent we mean neither the already existing human-scripted'mindless' chatterbots which follow a pre-programmed script to deceive [Mauldin, 1994], nor the'clueless' stochastic parrots [Bender et al., 2021] which blurt out sentences without having any sense of their meaning in-context, but AI agents in the likes of the conceptual machines that trick the judges in the Imitation Game [Turing, 1950].