arXiv AI By Lionel Levine

Math for AI safety: an invitation for mathematicians

Read the original on arXiv AI →

The article "Math for AI safety: an invitation for mathematicians" calls for new mathematical tools to ensure AI remains understandable, controllable, and cooperative. It outlines specific mathematical fields—logic, game theory, probability, algebra, representation theory, analysis, and geometry—each paired with an open problem tailored for mathematicians without AI safety background. The piece invites researchers to contribute to designing AI that is legible, steerable, and aligned with human values.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Oct 21

Fundamental Impossibility Results in AI Safety: Trading Off between Desiderata

The paper investigates whether safe AI systems can simultaneously satisfy multiple desiderata, such as harmlessness and helpfulness. By modeling the interaction between an AI, a defender, and an attacker as a three‑player game, the authors prove a fundamental tradeoff: advice that benefits the defender must remain public, limiting its safety guarantees. They also explore whether the AI can manipulate information to favor only the defender and discuss implications for safer AI design.

By Ida Mattsson, Ratip Emin Berker, Emanuel Tewolde, Ioannis Anagnostides, Kiriaki Fragkia, Xinyu Liu, Alexander Heckett, Jiayuan Liu, Eden Hartman, Vojt\v{e}ch Kova\v{r}\'{i}k, Sven Neth, Maria-Florina Balcan, Tuomas Sandholm, Vincent Conitzer
OpenAI Blog
Jun 21, 2016

Concrete AI safety problems

We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.