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Rethinking open source generative AI: open-washing and the EU AI Act

Authors:

Andreas Liesenfeld,

Mark DingemanseAuthors Info & Claims

FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency

Pages 1774 - 1787

https://doi.org/10.1145/3630106.3659005

Published: 05 June 2024 Publication History

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Abstract

The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are ‘open weight’ at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.

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Cited By

Osborne CDing JKirk H(2024)The AI community building the future? A quantitative analysis of development activity on Hugging Face HubJournal of Computational Social Science10.1007/s42001-024-00300-8Online publication date: 24-Jun-2024
https://doi.org/10.1007/s42001-024-00300-8

Index Terms

Rethinking open source generative AI: open-washing and the EU AI Act
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Open source software

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FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency

June 2024

2580 pages

ISBN:9798400704505

DOI:10.1145/3630106

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FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency

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Osborne CDing JKirk H(2024)The AI community building the future? A quantitative analysis of development activity on Hugging Face HubJournal of Computational Social Science10.1007/s42001-024-00300-8Online publication date: 24-Jun-2024
https://doi.org/10.1007/s42001-024-00300-8

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