Beyond Compute: A Weighted Framework for AI Capability Governance

Eleanor Nell Watson

2025

Abstract

Current AI governance metrics, focused primarily on computational power, fail to capture the full spectrum of emerging AI risks and capabilities, which risks significant unintended consequences. This analysis explores critical alternative paradigms including logic-based scaffolding techniques, graph search algorithms, agent ensembles, mixture-of-experts architectures, distributed training methods, and novel computing approaches such as biological organoids and photonic systems. By examining these as multidimensional weighted factors, this research aims to expand the discourse on AI progress beyond compute-centric models, culminating in actionable policy recommendations to strengthen frameworks like the EU AI Act in addressing the diverse challenges of AI development.

Paper Citation

in Harvard Style

Watson E. (2025). Beyond Compute: A Weighted Framework for AI Capability Governance. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 318-325. DOI: 10.5220/0013128800003890

in Bibtex Style

@conference{icaart25,
author={Eleanor Watson},
title={Beyond Compute: A Weighted Framework for AI Capability Governance},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={318-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013128800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Beyond Compute: A Weighted Framework for AI Capability Governance
SN - 978-989-758-737-5
AU - Watson E.
PY - 2025
SP - 318
EP - 325
DO - 10.5220/0013128800003890
PB - SciTePress