arXiv Machine Learning By Nikita Tsoy

Market Incentives for AI Safety Investment

Read the original on arXiv Machine Learning →

The paper examines how profit motives influence safety investment in AI, focusing on a supply chain with an upstream large language model (LLM) provider and multiple downstream firms. Using a game‑theoretic model, it shows that downstream competition tends to drive firms to invest more in safety than would maximize industry profit, while the upstream provider may underinvest because it does not capture all the benefits of safer models. The study also explores how regulatory interventions could shift incentives and affect overall model safety.

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 Machine Learning.

OpenAI Blog
Jul 10, 2019

Why responsible AI development needs cooperation on safety

We’ve written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. Our analysis shows that industry cooperation on safety will be instrumental in ensuring that AI systems are safe and beneficial, but competitive pressures could lead to a collective action problem, potentially causing AI companies to under-invest in safety.

arXiv AI
Jul 20

Harmonizing AI Safety Thresholds

arXiv:2607. 16112v1 Announce Type: new Abstract: Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies.

By Wilber Sean Anterola, Matthew Ball, Luis F. Lafuerza, Markov Grey
arXiv Machine Learning
Aug 4

Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems

arXiv:2603. 24742v2 Announce Type: replace-cross Abstract: As the capabilities and adoption of Artificial Intelligence (AI) systems grow, trust in these AI systems is an increasingly urgent concern.

By Adeela Bashir, Zhao Song, Ndidi Bianca Ogbo, Nataliya Balabanova, Martin Smit, Chin-wing Leung, Paolo Bova, Manuel Chica Serrano, Dhanushka Dissanayake, Manh Hong Duong, Elias Fernandez Domingos, Nikita Huber-Kralj, Marcus Krellner, Andrew Powell, Stefan Sarkadi, Fernando P. Santos, Zia Ush Shamszaman, Chaimaa Tarzi, Paolo Turrini, Grace Ibukunoluwa Ufeoshi, Victor A. Vargas-Perez, Alessandro Di Stefano, Simon T. Powers, The Anh Han