Unmasking the Shadows of AI: Investigating Deceptive Capabilities in
  Large Language Models
Linge Guo
This research critically navigates the intricate landscape of AI deception,
concentrating on deceptive behaviours of Large Language Models (LLMs). My
objective is to elucidate this issue, examine the discourse surrounding it, and
subsequently delve into its categorization and ramifications. The essay
initiates with an evaluation of the AI Safety Summit 2023 (ASS) and
introduction of LLMs, emphasising multidimensional biases that underlie their
deceptive behaviours.The literature review covers four types of deception
categorised: Strategic deception, Imitation, Sycophancy, and Unfaithful
Reasoning, along with the social implications and risks they entail. Lastly, I
take an evaluative stance on various aspects related to navigating the
persistent challenges of the deceptive AI. This encompasses considerations of
international collaborative governance, the reconfigured engagement of
individuals with AI, proposal of practical adjustments, and specific elements
of digital education.