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A Forgotten Manifesto: Mozilla Betrays Its Own Values on Open Source AI - Sam Johnston

Dec 22, 2024 - samjohnston.org
The article criticizes the Mozilla Foundation for supporting the Open Source Initiative's (OSI) Open Source AI Definition (OSAID), which excludes data transparency as a requirement for open-source AI. This stance is seen as contradictory to Mozilla's principles of openness and transparency, as outlined in its manifesto. The author argues that without access to training data, AI models cannot be truly open, as it prevents verification of fairness, bias, and security, and limits the ability to study and modify AI systems. The article calls for Mozilla to realign its support towards a definition of open-source AI that includes data transparency, emphasizing the importance of maintaining the integrity of the open internet and ensuring AI's future is open and accountable.

The article also highlights the broader industry rejection of OSAID, with criticisms from various open-source communities and leaders, including Bruce Perens, co-founder of OSI. It argues that the OSI's process lacked transparency and community involvement, favoring commercial interests over public benefit. The author urges Mozilla to reconsider its endorsement of OSAID and advocate for a truly open and community-driven definition of open-source AI, aligning with the principles of the Open Source Definition and ensuring both framework and training data are open.

Key takeaways:

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  • The Mozilla Foundation's support for the Open Source AI Definition (OSAID) is seen as contradictory to its principles of openness and transparency, as OSAID does not require data transparency.
  • Critics argue that excluding data from the definition of open-source AI undermines the collaborative spirit of the internet and consolidates power among a few corporations.
  • There is a call for Mozilla to advocate for a truly open and community-driven definition of open-source AI that includes data transparency, aligning with the principles of the Open Source Definition.
  • The article emphasizes the importance of data openness in AI to ensure fairness, accountability, and the ability to study and modify AI systems.
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