Is AI dangerous?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   A Systematic Literature Review on AI Safety: Identifying Trends, Challenges, and Future Directions

   IEEE Access (Salhab et al.)Published Jan 1, 2024Checked Oct 10, 2026

   “Artificial intelligence (AI) is revolutionizing many aspects of our lives, except it raises fundamental safety and ethical issues. In this survey paper, we review the current state of research on safe and trustworthy AI. This work provides a structured and systematic overview of AI safety. In which, we emphasize the significance of designing AI systems with safety focus, encompassing elements from data management, model development, and deployment. We underscore the need for AI systems to align with human values and operate within mounted ethical frameworks. In addition, we notice the need for a complete safety framework that courses the development and implementation of AI systems, ensuring they do not inadvertently cause damage to humans. Our results show that AI safety is associated with model learning techniques, verification and validation methods, failure modes, and managing AI autonomy. As discussed in the literature, the main concerns include explainability, interpretability, robustness, reliability, fairness, bias, and adversarial attacks.”

2. 2

   Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.

   Frontiers in digital health (Ferreira & Rosa)Published Sep 18, 2026Checked Oct 10, 2026

   “The evidence base was dominated by benchmark and simulated-workflow studies (89%), with limited prospective workflow-embedded evaluations (11%). Reported benefits concentrated on documentation efficiency, text quality, and knowledge synthesis; safety-relevant risks included hallucinated content, omission of clinically critical information, demographic bias, privacy vulnerabilities, limited explainability, and automation bias. Studies were geographically concentrated in North America and East Asia, with limited representation from Sub-Saharan Africa, South Asia, and Latin America.ConclusionsCurrent evidence supports cautious deployment of LLMs in selected healthcare tasks under structured oversight. Translational progress depends on prospective evaluation, standardised reporting, equity-focused audits, and lifecycle governance with continuous monitoring. The proposed five-dimensional framework (technical performance, clinical validity, equity, workflow integration, governance) coupled with a three-tier risk model is intended to support researchers and healthcare organisations in assessing readiness and implementing LLM-enabled tools responsibly.”

3. 3

   The risks associated with Artificial General Intelligence: A systematic review

   Journal of Experimental & Theoretical Artificial Intelligence (McLean et al.)Published Aug 13, 2021Checked Oct 10, 2026

   “The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen articles were deemed eligible for inclusion. Article types included in the review were classified as philosophical discussions, applications of modelling techniques, and assessment of current frameworks and processes in relation to AGI. The review identified a range of risks associated with AGI, including AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks. Several limitations of the AGI literature base were also identified, including a limited number of peer reviewed articles and modelling techniques focused on AGI risk, a lack of specific risk research in which domains that AGI may be implemented, a lack of specific definitions of the AGI functionality, and a lack of standardised AGI terminology. Recommendations to address the identified issues with AGI risk research are required to guide AGI design, implementation, and management.”

4. 4

   Artificial General Intelligence, Existential Risk, and Human Risk Perception

   arXiv (Cornell University) (Mandel)Published Nov 15, 2023Checked Oct 10, 2026

   “Artificial general intelligence (AGI) does not yet exist, but given the pace of technological development in artificial intelligence, it is projected to reach human-level intelligence within roughly the next two decades. After that, many experts expect it to far surpass human intelligence and to do so rapidly. The prospect of superintelligent AGI poses an existential risk to humans because there is no reliable method for ensuring that AGI goals stay aligned with human goals. Drawing on publicly available forecaster and opinion data, the author examines how experts and non-experts perceive risk from AGI. The findings indicate that the perceived risk of a world catastrophe or extinction from AGI is greater than for other existential risks. The increase in perceived risk over the last year is also steeper for AGI than for other existential threats (e.g., nuclear war or human-caused climate change). That AGI is a pressing existential risk is something on which experts and non-experts agree, but the basis for such agreement currently remains obscure.”

5. 5

   Privacy and Artificial Intelligence

   IEEE Transactions on Artificial Intelligence (Curzon et al.)Published Apr 1, 2021Checked Oct 10, 2026

   “For the purpose of this research, a North American perspective of privacy is adopted. Impact statement-While an appreciation of the privacy risks associated with artificial intelligence is important, a thorough understanding of the assortment of different technologies that comprise artificial intelligence better prepares those implementing such systems in assessing privacy impacts. This can be achieved through the independent consideration of each constituent of an artificially intelligent system and its interactions. Under individual consideration, privacy-enhancing tools can be applied in a targeted manner to reduce the risk associated with specific components of an artificially intelligent system. A generalized North American approach to assess privacy risks in such systems is proposed that will retain applicability as the field of research evolves and can be adapted to account for various sociopolitical influences. With such an approach, privacy risks in artificial intelligent systems can be well understood, measured, and reduced.”

6. 6

   Systematic Review on AI Safety and Future Research Agenda

   International Conference on System Reliability and Safety Engineering (SRSE) (Zhang & Lu)Published Nov 20, 2025Checked Oct 10, 2026

   “The Artificial Intelligence (AI) safety is critical for ensuring dependable deployment and mitigating risks in modern products and systems. This study conducts a systematic literature review using bibliometric analysis to map the evolution, key themes, and research gaps in AI safety. The bibliometrix R package was employed to analyze 123 peerreviewed articles (2014-2025) from Web of Science. The analysis included keywords co-occurrence, thematic evolution mapping, and collaboration network assessment. The USA and China lead in the publication number, but the influence of the USA is unique globally. Meanwhile, cross-disciplinary collaboration remained limited, especially computer science field almost dominate the contribution while safety engineering discipline rarely involved. Finally, a five-point multilevel research agenda was proposed to guide future emerging challenges and adaptive regulation.”

7. 7

   How Does Artificial Intelligence Pose an Existential Risk?

   Oxford University Press eBooks (Vold & Harris)Published Nov 10, 2021Checked Oct 10, 2026

   “Alan Turing, one of the fathers of computing, warned that artificial intelligence (AI) could one day pose an existential risk to humanity. Today, recent advancements in the field of AI have been accompanied by a renewed set of existential warnings. But what exactly constitutes an existential risk? And how exactly does AI pose such a threat? In this chapter, we aim to answer these questions. In particular, we will critically explore three commonly cited reasons for thinking that AI poses an existential threat to humanity: the control problem, the possibility of global disruption from an AI race dynamic, and the weaponization of AI.”