Published: 27 November 2024
*Why ‘open’ AI systems are actually closed, and why this matters
*
David Gray Widder, Meredith Whittaker & Sarah Myers West
/
Nature/ *635*, 827–833 (2024). https://doi.org/10.1038/s41586-024-08141-1
Abstract
This paper examines ‘open’ artificial intelligence (AI). Claims about
‘open’ AI often lack precision, frequently eliding scrutiny of
substantial industry concentration in large-scale AI development and
deployment, and often incorrectly applying understandings of ‘open’
imported from free and open-source software to AI systems. At present,
powerful actors are seeking to shape policy using claims that ‘open’ AI
is either beneficial to innovation and democracy, on the one hand, or
detrimental to safety, on the other. When policy is being shaped,
definitions matter. To add clarity to this debate, we examine the basis
for claims of openness in AI, and offer a material analysis of what AI
is and what ‘openness’ in AI can and cannot provide: examining models,
data, labour, frameworks, and computational power. We highlight three
main affordances of ‘open’ AI, namely transparency, reusability, and
extensibility, and we observe that maximally ‘open’ AI allows some forms
of oversight and experimentation on top of existing models. However, we
find that openness alone does not perturb the concentration of power in
AI. Just as many traditional open-source software projects were co-opted
in various ways by large technology companies, we show how rhetoric
around ‘open’ AI is frequently wielded in ways that exacerbate rather
than reduce concentration of power in the AI sector.
https://www.nature.com/articles/s41586-024-08141-1
