Why we need an AI-resilient society

TL;DR

This paper discusses the societal impact of AI, characterizes its systemic risks using forensic-psychology profiling, and proposes a three-pillar framework to enhance AI resilience and preserve human agency.

Contribution

It introduces a novel forensic-psychology profiling methodology for AI and proposes a comprehensive framework for AI resilience focused on human judgment and control.

Findings

AI exhibits hallucinations, bias, and lack of causal understanding.

Large language models pose systemic risks like misinformation and erosion of expertise.

The proposed framework aims to safeguard societal institutions and human decision-making.

Abstract

Three generations of software have transformed the role of artificial intelligence in society. In the first, programmers wrote explicit logic; in the second, neural networks learned programs from data; in the third, large language models turn natural language itself into a programming interface. These shifts have consequences that reach far beyond computer science, reshaping how societies generate knowledge, make decisions, and govern themselves. While generative adversarial networks introduced the era of deepfakes and synthetic media, large language models have added an entirely new class of systemic risks. This report applies a forensic-psychology profiling methodology to characterize AI based on nine documented features: hallucinations, bias and toxicity, sycophancy and echo chambers, fabrication and credulity, knowledge without understanding, discontinuity and the inability to learn from experience, jagged intelligence and scaling limits, shortcuts and fractured representations, and cognitive atrophy. The resulting profile reveals an "entity" that confabulates fluently, mirrors its users' biases, possesses encyclopedic recall without causal understanding, and erodes the competence of those who depend on it. The implications extend to institutional erosion across law, academia, journalism, and democratic governance. To address these challenges, this report proposes a three-pillar framework for AI resilience: cognitive sovereignty, which preserves the capacity for independent judgment; measurable control, which translates ethical commitments into enforceable standards and red lines; and partial autonomy, which maintains human agency at critical decision points. This report is an updated and extended version of arXiv:1912.08786v1.