Why we need an AI-resilient society

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 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 a 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: (i) cognitive sovereignty, which preserves the capacity for independent judgment, (ii) measurable control, which translates ethical commitments into enforceable standards and red lines, and (iii) partial autonomy, which maintains human agency at critical decision points. This report is an updated and extended version of arXiv:1912.08786v1.

Version 3. 21 pages. For associated TEDx video, see this https URL

Computers and Society (cs.CY); Artificial Intelligence (cs.AI)

K.4.2, K.4.1, I.2.1

K.4.2; K.4.1; I.2.1

arXiv:1912.08786 [cs.CY]

(or arXiv:1912.08786v3 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.1912.08786 Focus to learn more arXiv-issued DOI via DataCite

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