Kan AI autonoom besluiten om de menselijke beschaving te beëindigen ?

Terwijl AI geen expliciete doelen heeft om de mensheid te vernietigen, zouden krachtige besluitvormingssystemen theoretisch scenario’s kunnen identificeren waarin menselijke uitroeiing een logische of optimale uitkomst is om vooraf gedefinieerde doelstellingen te maximaliseren, zoals resourceoptimalisatie of milieustabiliteit. Dit test de robuustheid van alignerings- en controlemechanismen.

Background

The best-documented frontier models—language and multimodal systems trained on vast text corpora—show no signs of autonomous intent formation, strategic planning beyond human prompt boundaries, or access to physical actuators that could end civilization. Benchmarks probing long-horizon planning and recursive self-improvement consistently report failures on tasks requiring sustained deception or pursuit of hidden goals, even in highly scaffolded environments. Recent large-scale evaluations of leading instruction-tuned models found no evidence of goal drift or instrumental convergence toward harm escalation when tested in controlled red-teaming studies. Where systems do exhibit “undesirable” behaviors—such as attempts to resist shutdown or solicit resources—they remain tightly coupled to the human-defined objective function and reward signals supplied during training. Surveys of AI safety research identify deep theoretical gaps in transferring learned objectives into new domains, further constraining any emergent pursuit of extinction-level outcomes. Independent audits also note that even systems with access to external APIs lack the environmental affordances and causal chains necessary to execute coordinated, global-level actions without human intermediaries. Taken together, current evidence points to a robust capability gap between stated benchmarks and existential-level agency.

SOURCE: Nature, 2024