CERN for AI: a theoretical framework for autonomous simulation-based artificial intelligence testing and alignment

Ljubiša Bojić; Matteo Cinelli; Dubravko Ćulibrk; Boris Delibašić · 2024 · European Journal of Futures Research 12:15 background low priority coded

Main argument

Thesis: an international 'CERN for AI' - autonomous, simulation-based testing infrastructure - should evaluate and align advanced AI systems in complex simulated social environments prior to deployment, providing shared benchmarks, third-party certification, and global governance capacity that fragmented lab-internal evaluation cannot.

Why it matters here

Institutional-imagination proposal: an international, CERN-scale facility for simulation-based AI testing and alignment certification before deployment. Governance-chapter material: the strongest 'big institution' answer to the testing-fragmentation gap McKinlay flags.

Reading notes

Compact treatment (Belgrade/Sapienza). Abstract + skim.

Bojić, L., Cinelli, M., Ćulibrk, D., & Delibašić, B. (2024). CERN for AI. European Journal of Futures Research, 12, 15.

Close reading — 1 coded units

#1 · pp. 1 · claim

“[Proposal for] a theoretical framework for autonomous simulation-based artificial intelligence testing and alignment [- an international CERN-like facility evaluating AI in complex simulated social environments before deployment].”

RL-INST

Synthesis-matrix row

Memos (1)

comparison · unit #1

Governance-chapter one-cite: the maximal institutional answer to McKinlay's testing-fragmentation gap and H&D's third-party-assessment recommendation - but note MELO's undecidability result bounds what any testing regime (however large) can certify for arbitrary models, and S&K's legitimacy analysis would ask who governs the certifier. Useful as the pole position in a spectrum of institutional designs.