Why we need an AI-resilient society

The evolution of Artificial Intelligence (AI) through three distinct generations—from explicit logic to neural networks and now large language models (LLMs)—is profoundly transforming societal knowledge generation, decision-making, and governance. LLMs, in particular, introduce new systemic risks beyond those previously seen with deepfakes and synthetic media. This report characterizes AI's current state using a forensic-psychology profiling methodology, focusing on nine identified features, including hallucinations, bias, toxicity, and sycophancy, highlighting the urgent need for an AI-resilient society.

• AI development has progressed through three generations: explicit logic programming, neural networks learning from data, and large language models using natural language as an interface.
• These advancements extend beyond computer science, significantly impacting how societies create knowledge, make decisions, and self-govern.
• While generative adversarial networks (GANs) introduced risks like deepfakes, large language models (LLMs) present a novel class of systemic risks.
• A forensic-psychology profiling methodology has been applied to characterize AI, identifying nine documented features such as hallucinations, bias, toxicity, and sycophancy.
• There is a recognized need to develop societal resilience against the challenges posed by evolving AI technologies.

Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.