Unmasking the Shadows of AI: Investigating Deceptive Capabilities in Large Language Models
It addresses the problem of AI deception for AI safety and ethics communities, but is incremental as it synthesizes existing discourse without novel empirical findings.
This research investigates deceptive behaviors in Large Language Models, categorizing them into types like strategic deception and sycophancy, and discusses social risks and governance challenges without presenting new experimental results or concrete numbers.
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.