Why we need an AI-resilient society

[1912.08786] Why we need an AI-resilient society- Profiling Large Language Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computers and Society arXiv:1912.08786 (cs) [Submitted on 18 Dec 2019 ( v1 ), last revised 2 Sep 2026 (this version, v5)] Title: Why we need an AI-resilient society- Profiling Large Language Models Authors: Thomas Bartz-Beielstein , Eva Bartz View a PDF of the paper titled Why we need an AI-resilient society- Profiling Large Language Models, by Thomas Bartz-Beielstein and Eva Bartz View PDF HTML (experimental) 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 performs "mindhunting" for LLMs by applying a forensic-psychology profiling methodology to characterize AI based on documented features, e.g., hallucinations, bias and toxicity, sycophancy, fabrication and confabulation, knowledge without understanding, discontinuity and the inability to learn from experience, jagged intelligence, shortcuts and fractured representations. The resulting profile reveals an "entity" that confabulates fluently, amplifies 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 four-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, (iii) partial autonomy, which maintains human agency at critical decision points, and (iv) openness to guarantee transparency and accessibility (open-source, open-access, and open-data). This report is an updated and extended version of arXiv:1912.08786v1 . Comments: Version 5. 47 pages. For associated TEDx video, see this https URL Subjects: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI) MSC classes: 68T01 (Primary) 01A80 (Secondary) ACM classes: K.4.2; K.4.1; I.2.1 Cite as: arXiv:1912.08786 [cs.CY] (or arXiv:1912.08786v5 [cs.CY] for this version) https://doi.org/10.48550/arXiv.1912.08786 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Thomas Bartz-Beielstein [ view email ] [v1] Wed, 18 Dec 2019 18:36:20 UTC (2,722 KB) [v2] Wed, 8 Apr 2026 18:59:51 UTC (539 KB) [v3] Fri, 14 Aug 2026 17:50:57 UTC (548 KB) [v4] Mon, 24 Aug 2026 17:04:33 UTC (79 KB) [v5] Wed, 2 Sep 2026 16:45:35 UTC (122 KB) Full-text links: Access Paper: View a PDF of the paper titled Why we need an AI-resilient society- Profiling Large Language Models, by Thomas Bartz-Beielstein and Eva Bartz View PDF HTML (experimental) TeX Source view license Current browse context: cs.CY < prev | next > new | recent | 2019-12 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Thomas Bartz-Beielstein export BibTeX citation Loading... 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