Advancements in AI capabilities, driven in large part by scaling up computing resources used for AI training, have created opportunities to address major global challenges but also pose risks of misuse. Hardware-enabled mechanisms (HEMs) can support responsible AI development by enabling verifiable reporting of key properties of AI training activities such as quantity of compute used, training cluster configuration or location, as well as policy enforcement. Such tools can promote transparency and improve security, while addressing privacy and intellectual property concerns. Based on insights from an interdisciplinary workshop, we identify open questions regarding potential implementation approaches, emphasizing the need for further research to ensure robust, scalable solutions.
View on arXiv@article{o'gara2025_2505.03742,
title={ Hardware-Enabled Mechanisms for Verifying Responsible AI Development },
author={ Aidan O'Gara and Gabriel Kulp and Will Hodgkins and James Petrie and Vincent Immler and Aydin Aysu and Kanad Basu and Shivam Bhasin and Stjepan Picek and Ankur Srivastava },
journal={arXiv preprint arXiv:2505.03742},
year={ 2025 }
}