As part of the action, the Open Source Initiative (OSI) was proud to sponsor the event and be represented by Stefano Maffulli, OSI’s Executive Director, and Jordan Maris, OSI’s EU Policy Analyst. Jordan joined a standout lineup of speakers and facilitators — from Frank Karlitschek of Nextcloud to Amandine Le Pape of Element — all focused on helping Open Source founders overcome the unique challenges of building in the open.

Unpacking the CRA: What Open Source founders need to know

Jordan led a workshop titled “The Ramifications of the Cyber Resilience Act,” offering founders a practical lens into navigating one of the most pressing laws affecting Open Source in Europe today.

The Cyber Resilience Act (CRA) introduces new legal obligations for software producers operating in Europe, including Open Source projects and companies. During the session, Jordan helped attendees:

• Explore how the regulation defines “commercial activity” and what constitutes a “product with digital elements”
  
• Explore how different Open Source business models may or may not fall under the CRA’s scope
  
• Work through real-world scenarios, discuss strategies to adapt development and compliance practices, and better understand the legal and operational risks involved
  
• Learn how the Open Source community is responding to the CRA and what collective actions are emerging to influence its implementation

Strengthening the Open Source ecosystem

This summit was more than just talks and workshops — it was a space for Open Source founders to connect over shared experiences, swap lessons learned, and co-create solutions to the ecosystem’s toughest challenges. Whether it was grappling with go-to-market strategies, managing dual audiences of users and customers, or understanding the regulatory landscape, the event delivered real value.

The OSI’s presence at the event underscores our commitment to supporting Open Source not only through definitions and licensing, but by helping communities navigate the increasingly complex legal and business terrain.

From choosing the right license to structuring project governance, understanding compliance, and identifying paths to financial sustainability, OSI is a partner to Open Source founders at every step. We provide resources, host conversations, and advocate for policies that protect and empower open innovation.

Looking ahead

OSI will remain on the frontlines — informing, convening, and advocating for business best practices and policies that support the long-term health of Open Source.

We’re grateful to the Open Source Founders Summit team for creating such an energizing and focused event, and we look forward to continuing these important conversations in the months to come.

Stay tuned for more updates from OSI’s team by subscribing to our blog — and if you’re an Open Source founder, we’d love to hear from you.

Resources

Slides from Jordan’s workshop “The Ramifications of the Cyber Resilience Act” are available below:

The OSI thanks all of those who participated in the 2025 board polls by asking questions to the candidates, engaging in discussions, and casting a ballot. Three candidates have been excluded from the final tally: Two were ineligible as they did not sign the current  board agreement; one returned the signed agreement after the deadline passed. The Open Source Initiative is committed to upholding its mission while adapting to the evolving landscape of Open Source.

Next steps

We look forward to welcoming the new Directors at the April board meeting.

The board has directed the Board Development committee to run a retrospective by April 19, 2025 and bring to the board any recommendations for the future.

Complete elections results

OSI Affiliate directors polls 2025

Counting ballots using Scottish STV. There are 4 valid candidates competing for 2 seats. The number of ballots is 48 and there were 47 valid ballots and 1 empty ballot.

Winners are Carlo Piana and Ruth Suehle.

OSI Individual directors polls 2025

Counting ballots using Scottish STV. There are 5 valid candidates competing for 1 seat. The number of ballots is 159 and there were 148 valid ballots and 11 empty ballots.

Winner is McCoy Smith.

Update: April 21, 2025

From the Board of Directors of the OSI

Since OSI introduced individual member and affiliate votes for some board seats in 2013, we have been in a continuous process of improving those elections.  This year, like in other election cycles, we discovered limitations and corner cases in our procedures. We renew our commitment to continuous improvement and have revised our internal procedures and templates to address the specific issues.

As we do each year, the OSI held an internal retrospective with directors and staff to reflect on public feedback, team observations, and technical difficulties, and identify action items for improvement. This retro included feedback given by several members via social media and discuss.opensource.org. We thank the members who provided this feedback; it helps us to identify problems that we would not have seen otherwise. Here are our takeaways from that process which involve the board and our members.

Improvements in communication

This election, as well as the OSAID process, made it apparent that our supporters want better channels to provide structured feedback to the Board about organizational direction, decisions, and priorities. Also, that it’s important to our supporters that feedback be clearly received and acknowledged, and that they receive word of any related board decision when one is made. Our elections are a poor vehicle for this kind of communication, so we must provide others.

The Board is working on a plan to reshape discussions with our supporters in order to realize better two-way communication. You can expect to hear more about this plan in upcoming months, and we hope you will help us improve it through your feedback.

As a smaller but nevertheless critical part of communication, we’ve been told by several of our members that they don’t understand the voting system we’re using, and it hasn’t been explained. In upcoming months, we will publish one or more articles and link to more materials explaining Single Transferable Vote and OpaVote, why we use them, and how your ballot expresses your opinion.

Improvements in candidate selection

In prior elections, we identified the issue that some candidates were not prepared for the actual requirements and duties of being on the OSI Board. We added candidate information sessions to the schedule and shared board materials with candidates in advance of the election. This was successful in allowing candidates to determine, themselves, whether they were ready to run for election, and it will remain a part of the election timeline.

What this election exposed was the need for the organization to also assess whether candidates were fully eligible to run and prepared to be seated on the board before voting begins. This is something we will add to the election timeline next year. While we have not finished figuring out all of the requirements for that assessment, part of it will be asking candidates to sign a Candidate Agreement* at nomination time. We also have some ideas on ways for potential candidates to have more information even before submitting a nomination.

The Board also noticed that, however successful we were in recruiting candidates (15 in this election), our candidate pool is not as representative of all of the different constituencies in our Open Source stakeholder groups as we would like it to be. We have begun working on some long-term plans to improve candidate diversity and involvement.

Conclusions

We acknowledge that we must improve all communications, so that we do not take for granted the level of familiarity any supporter may have with OSI’s work or its annual elections. OSI will improve transparency by creating better supporter engagement and feedback channels, clarifying the election process and candidate expectations, and enhancing public communication to be more inclusive and accessible.

* The board agreement governs our conduct as directors expected of leaders at OSI above and beyond, and the OSI code of conducts (License Review CoC, Event CoC, Discourse TOC) govern our whole community. To our knowledge, all involved with this election, even those who did not sign our board agreement, agreed to be held to the OSI code of conduct(s).

Two key COSCUP partners—g0v Jothon, a civic tech community, and the Open Culture Foundation (OCF), an OSI affiliate—spearheaded dedicated discussions on these themes. This article delves into their sessions, shedding light on how advocates in East Asia are addressing critical challenges in these Open Source landscapes.

Open Data for Inclusive AI

g0v Jothon spearheads the “Traditional Chinese AI Open Source Implementation Project,” aiming to advance AI Open Source development for Traditional Chinese. This initiative supports six teams building high-quality Open Source datasets across domains like benchmarking, law, healthcare, parliamentary data, education and Taiwanese language resources. At COSCUP 2024, the project showcased its datasets, outcomes and application demonstrations. To date, it has generated over 238,000 entries of Traditional Chinese Open Source data, accessible to the public here.

The participating teams are impressively diverse. Some are formed by IT professionals, while others are led by companies specializing in linguistic data analysis. Interdisciplinary collaborations are also prominent, including partnerships between journalism foundations and startups, IT research labs and hospital doctors, and teams co-organized by teachers and technical experts.

Building a Sustainable Open-Source AI Ecosystem

g0v Jothon also hosted a cross-disciplinary forum on the “GenAI Open Source Ecosystem.” Panelists included representatives from Taiwan’s large language model industry, AI startup community leaders, Open Source application platform advocates, and civil AI community organizers. Moderated by the Secretary-General of Taiwan AI Academy, the discussion emphasized the importance of Open Source principles and strategies for generative AI R&D.

The forum also stressed that social responsibility in technology requires equitable access. Large corporations engaging in closed-source development were encouraged to contribute to the Open Source ecosystem. The presentations and forum videos are available on the project’s website.

Breaking new ground with Open Source policy discussions

For the first time, the Open Culture Foundation (OCF) hosted an Open Source Policy track at COSCUP 2024. Inspired by insights from FOSDEM, OCF aimed to explore how policies—whether from governments, enterprises or communities—impact the Open Source ecosystem.

The policy track featured nine sessions attended by nearly 300 participants. The diverse speaker lineup included Open Source community leaders, NGO advocates, researchers and government officials.

Key sessions:

• My Experience Creating Open Source Projects in the Government
  TonyQ shared a compelling narrative of his journey from external collaboration with government agencies to becoming an internal advocate for Open Source initiatives within the public sector. His session notably explored why promising Open Source projects within government often fade away despite initial momentum, highlighting the structural and cultural challenges that need to be addressed for sustainable implementation.
• The Business Dilemma of Open Source Projects
  Date Huang provided a comprehensive analysis of Open Source business models, drawing from real-world examples. He examined various approaches, from support-based services to pay-for-specific-version models, detailing the specific challenges each faces. His discussion particularly resonated when addressing the ultimate challenge: maintaining project sustainability while preserving the core values of Open Source development.

While COSCUP has previously hosted policy-related discussions, these were often scattered across different tracks, making them challenging to follow systematically. The dedicated policy track revealed a pent-up demand for structured dialogue on Open Source policy matters. Particularly encouraging was the enthusiastic response to government representatives’ participation, with extended post-presentation discussions reflecting the community’s keen interest in public sector engagement.

The success of this first policy track suggests promising potential for future development. Looking ahead, organizers hope to establish it as a permanent fixture of COSCUP, expanding both the breadth of topics covered and the depth of discussion. This could include more focused sessions on specific policy issues and enhanced opportunities for substantive dialogue between stakeholders.

This new addition to COSCUP demonstrates the growing recognition that Open Source success depends not only on technical excellence but also on supportive policy frameworks and engaged stakeholder communities.

All about people: Why COSCUP is a key convergence point for Open Source

COSCUP, organized entirely by volunteers, is a vibrant hub for Open Source enthusiasts to exchange ideas. Now in its 20th year, it attracts 3,000 attendees annually. The event collaborates with global initiatives, such as the Celebrating 25 Years of Open Source campaign led by the OSI and the global Open Source survey led by the Linux Foundation. These partnerships underscore COSCUP’s significance within the global Open Source community, cementing its place as a favorite event among advocates.

Curious to learn more? Read COSCUP Unveiled by Paloma Oliveira.

For Inquiries:

• g0v Jothon: jothon-organizers@g0v.tw
• Open Culture Foundation: hi@ocf.tw
• COSCUP: engagement@coscup.org

You can also meet OCF and COSCUP in person at the upcoming FOSDEM25, where they will host a stand at the event.

This article is contributed by Amos Li from OCF, Che Wei Liu from g0v Jothon, and Peter H.R. Chu from COSCUP under CC BY 4.0.

“AI is arguably the most transformative technology of our generation,” said Stefano Maffulli, executive director, Open Source Initiative. “The challenge now is to craft policies that not only foster growth of AI but ensure that Open Source AI thrives within this evolving landscape. Partnering with the Eclipse Foundation and its expertise, with its experience in European open source development and regulatory compliance, is important to shape the future of Open Source AI.”

“For decades, OSI has been the ‘gold standard’ the open source community has turned to for building consensus around important issues,”  said Mike Milinkovich, executive director of the Eclipse Foundation. “As AI reshapes industries and societies, there is no more pressing issue for the open source community than the regulatory recognition of open source AI systems. Our combined expertise – OSI’s global leadership in open standards and open source licences and our extensive work with open source regulatory compliance – makes this partnership a  powerful advocate for the design and implementation of sound AI policies worldwide.”

Addressing the Global Challenges of AI Regulation

With AI regulation on the horizon in multiple regions, including the EU, both organisations recognise the urgency of helping policymakers understand the unique challenges and opportunities of OSAI technologies.  The rapid evolution of AI technologies, together with new, upcoming complex regulatory landscapes, demand clear, consistent, and aligned guidance rooted in open source principles.

Through this partnership, the Eclipse Foundation and OSI will endeavour to bring clarity in language and terms that industry, community, civil society, and policymakers can rely upon as public policy is drafted and enforced. The organisations will collaborate by leveraging their respective public platforms and events to raise awareness and advocate on the topic.  Additionally, they will work together on joint publications, presentations, and other promotional activities, while also assisting one another in educating government officials on policy considerations for OSAI and General Purpose AI (GPAI). Through this partnership, they aim to provide clear, consistent guidance that aligns with open source principles.

Key Areas of Collaboration

The MoU outlines several areas of cooperation, including:

• Information Exchange: OSI and the Eclipse Foundation will share relevant insights and information related to public policy-making and regulatory activities on artificial intelligence.
• Representation to Policymakers: OSI and the Eclipse Foundation will cooperate in representing the principles and values of open source licences to policymakers and civil society organisations.
• Promotion of Open Source Principles: Joint efforts will be made to raise awareness of the role of open source in AI, emphasising how it can foster innovation while mitigating risks. 

A Partnership for the Future

As AI continues to revolutionise industries worldwide, the need for thoughtful, balanced regulation is critical. The OSI and Eclipse Foundation are committed to providing the open source community, industry leaders, and policymakers with the tools and knowledge they need to navigate this rapidly evolving field.

This MoU marks the very beginning of a long-term collaboration, with joint initiatives and activities to be announced throughout the remainder of 2024 and into 2025. 

About the Eclipse Foundation

The Eclipse Foundation provides our global community of individuals and organisations with a business-friendly environment for open source software collaboration and innovation. We host the Eclipse IDE, Adoptium, Software Defined Vehicle, Jakarta EE, and over 420 open source projects, including runtimes, tools, specifications, and frameworks for cloud and edge applications, IoT, AI, automotive, systems engineering, open processor designs, and many others. Headquartered in Brussels, Belgium, the Eclipse Foundation is an international non-profit association supported by over 385 members. To learn more, follow us on social media @EclipseFdn, LinkedIn, or visit eclipse.org.

About the Open Source Initiative

Founded in 1998, the Open Source Initiative (OSI) is a non-profit corporation with global scope formed to educate about and advocate for the benefits of Open Source and to build bridges among different constituencies in the Open Source community. It is the steward of the Open Source Definition, setting the foundation for the global Open Source ecosystem. Join and support the OSI mission today at https://opensource.org/join. 

Third-party trademarks mentioned are the property of their respective owners.

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The release of version 1.0 was announced today at All Things Open 2024, an industry conference focused on common issues of interest to the worldwide Open Source community. The OSAID offers a standard by which community-led, open and public evaluations will be conducted to validate whether or not an AI system can be deemed Open Source AI. This first stable version of the OSAID is the result of multiple years of research and collaboration, an international roadshow of workshops, and a year-long co-design process led by the Open Source Initiative (OSI), globally recognized by individuals, companies and public institutions as the authority that defines Open Source.

“The co-design process that led to version 1.0 of the Open Source AI Definition was well-developed, thorough, inclusive and fair,” said Carlo Piana, OSI board chair. “It adhered to the principles laid out by the board, and the OSI leadership and staff followed our directives faithfully. The board is confident that the process has resulted in a definition that meets the standards of Open Source as defined in the Open Source Definition and the Four Essential Freedoms, and we’re energized about how this definition positions OSI to facilitate meaningful and practical Open Source guidance for the entire industry.”

“The new definition requires Open Source models to provide enough information about their training data so that a ‘skilled person can recreate a substantially equivalent system using the same or similar data,’ which goes further than what many proprietary or ostensibly Open Source models do today,” said Ayah Bdeir, who leads AI strategy at Mozilla. “This is the starting point to addressing the complexities of how AI training data should be treated, acknowledging the challenges of sharing full datasets while working to make open datasets a more commonplace part of the AI ecosystem. This view of AI training data in Open Source AI may not be a perfect place to be, but insisting on an ideologically pristine kind of gold standard that will not actually be met by any model builder could end up backfiring.”

“We welcome OSI’s stewardship of the complex process of defining Open Source AI,” said Liv Marte Nordhaug, CEO of the Digital Public Goods Alliance (DPGA) secretariat. “The Digital Public Goods Alliance secretariat will build on this foundational work as we update the DPG Standard as it relates to AI as a category of DPGs.”

“Transparency is at the core of EleutherAI’s non-profit mission. The Open Source AI Definition is a necessary step towards promoting the benefits of Open Source principles in the field of AI,” said Stella Biderman, executive director at the EleutherAI Institute. “We believe that this definition supports the needs of independent machine learning researchers and promotes greater transparency among the largest AI developers.”

“Arriving at today’s OSAID version 1.0 was a difficult journey, filled with new challenges for the OSI community,” said OSI Executive Director, Stefano Maffulli. “Despite this delicate process, filled with differing opinions and uncharted technical frontiers—and the occasional heated exchange—the results are aligned with the expectations set out at the start of this two-year process. This is a starting point for a continued effort to engage with the communities to improve the definition over time as we develop with the broader Open Source community the knowledge to read and apply OSAID v.1.0.”

The text of the OSAID v.1.0 as well as a partial list of the many global stakeholders who endorse the definition can be found here: https://opensource.org/ai

About the Open Source Initiative
Founded in 1998, the Open Source Initiative (OSI) is a non-profit corporation with global scope formed to educate about and advocate for the benefits of Open Source and to build bridges among different constituencies in the Open Source community. It is the steward of the Open Source Definition and the Open Source AI Definition, setting the foundation for the global Open Source ecosystem. Join and support the OSI mission today at: https://opensource.org/join.

To have the chance for powerful Open Source AI systems to exist in any domain, the OSI community has incorporated in the Definition this principle: 

An Open Source AI needs to make available three kinds of components: the software used to create the dataset and run the training, the model parameters and the code to run inference, and finally all the data that can be made available legally.

Recognizing that there are four kinds of “data”, each with its own legal frameworks allowing different freedoms of distribution, we bypass what Stephen O’Grady called the “AI conundrums” and give Open Source AI builders a chance to build freedom-respecting alternatives to pretty much any proprietary AI.

Limiting Open source AI only to systems trainable on freely distributable data would relegate Open Source AI to a niche. One of which is that the amount of freely and legally shareable data is a tiny fraction of what is necessary to train powerful systems. Additionally, it’d be excluding Open Source AI from areas where data cannot be shared, like medical or anything dealing with personal or private data. What remains for “Open Source AI” would be tiny. There are abundant motives to reject this limitation.

The fact is, mixing openly distributable and non-distributable data is very similar to a reality we are very familiar with: Open Source software built with proprietary compilers and system libraries.

Is GNU Emacs Open Source software?

I’m sure you’d answer yes (and some of you will say “well, actually it’s free software”) and we’ll all agree. Below is a rough diagram of Emacs built for the GNOME desktop on a modern Linux distribution. Emacs depends on a few system libraries that GNOME provides with OSI-Approved Licenses. The whole stack is Open Source these days and one can distribute Emacs on a disk with all its dependencies without too much legal trouble. Imagine scientists who want to freeze the whole environment of an experiment they made; they could package all the pieces of a system like this without trouble and distribute it all with their paper. No problem here.

Now let’s go back to an age when Linux systems weren’t ready. When Stallman started writing Emacs, there was no GNOME and no Linux, no gcc and no glibc. He thought very early on that in order to have more freedom, he had to create a wedge to allow Emacs to run on proprietary software.

Emacs on the latest Solaris versions would look something like this: some pieces like X11 and Gstreamer are Open Source. Others, like libc and others aren’t. The hypothetical scientists from before couldn’t really freeze their full scientific environment. All they could say in their paper was: “We used Emacs from this CVS version, built with gcc version X with these makefile; tar.gz attached” and make a list of the operating system’s version and libraries versions they used. That’s because they have the right only to distribute Emacs, X11, some libraries and not the rest of Solaris.

Is Emacs on Solaris Open Source? Of course it is, even though the source code for the system libraries are not available.

One more question, Emacs on Mac OS: it can only be built with a proprietary compiler on proprietary GUI and other proprietary libraries.

Is Emacs on Mac Open Source? Of course it is. Can you fully study Emacs on Mac OS? For Emacs, yes. For the MacOS components, no. There are many programs that run only on MacOS or Windows: for OSI, those are Open Source. Would someone argue that they’re not “really Open Source” because you can’t see “everything?” Some people might but we’ve learned to live with that, adding governance rules in addition to those of the Open Source Definition. Debian for example requires that programs are Open Source and support multiple hardware platforms; the ASF graduates only projects that are Open Source and have a diverse community of contributors. If you only want to use Open Source applications running on Open Source stacks, you can decide that! Just as you can decide that your company will only acquire Open Source software whose copyright is owned by multiple entities. 

These are all additional requirements built on top of the base floor set by the Open Source Definition.

For AI, you can do the same: You can say “I will only use Open Source AI built with open data, because I don’t want to trust anything less than that.” A large organization could say “I will buy only Open Source AI that allows me to audit their full dataset, including unshareable data.” You can do all that. Open Source AI is the floor that you can build on, like the OSD.

Bypassing the conundrums

We’ve looked for a solution for almost three years and this is it: Require all the data that is legally shareable, and for the other data provide all the details. It’s exactly what we’ve been doing for Open Source software: 

You developed a text editor for Mac OS but you can’t share the system libraries? Fine, we’ll fork it: give us all the code you can legally share with an OSI-Approved License and we’ll rip the dependencies and “liberate” it to run on GNU. The editor will be slightly different, like code that runs on some ARM+Linux systems behaves differently on Intel+Windows for the different capabilities of the underlying hardware and OS, but it’s still Open Source.

For Open Source AI it’s a similar dance: You can’t legally give us all the data? Fine, we’ll fork it. For example, you made an AI that recognizes bone cancer in humans but the data can’t be shared. We’ll fork it! Tell us exactly how you built the system, how you trained it, share the code you used, and an anonymized sample of the data you used so we can train on our X-ray images. The system will be slightly different but it’s still Open Source AI.

If we want to have broad availability of powerful alternatives to proprietary AI systems that respect the freedoms of users and deployers, we must recognize conditions that make sense for the domain of AI. These examples of proprietary compilers and system libraries used to build Open Source software prove that there is room for similar conditions when talking about Code, Data and Parameters within the definition of Open Source AI.

There are three relevant changes to the part of the definition pertaining to the “preferred form to make modifications to a machine learning system.”

The feature that will draw most attention is the new language of Data Information. It clarifies that all the training data needs to be shared and disclosed. The updated text comes from many conversations with several individuals who engaged passionately with the design process, on the forum, in person and on hackmd. These conversations helped describe four types of data: open, public, obtainable and unshareable data, well described in the FAQ. The legal requirements are different for each. All are required to be shared in the form that the law allows them to be shared. 

Two new features are equally important. RC1 clarifies that Code must be complete, enough for downstream recipients to understand how the training was done. This was done to reinforce the importance of the training, both for transparency, security and other practical reasons. Training is where innovation is happening at the moment and that’s why you don’t see corporations releasing their training and data processing code. We believe, given the current status of knowledge and practice, that this is required to meaningfully fork (study and modify) AI systems.

Last, there is new text that is meant to explicitly acknowledge that it is admissible to require copyleft-like terms for any of the Code, Data Information and Parameters, individually or as bundled combinations. A demonstrative scenario is a consortium owning rights to training code and a dataset deciding to distribute the bundle code+data with legal terms that tie the two together, with copyleft-like provisions. This sort of legal document doesn’t exist yet but the scenario is plausible enough that it deserves consideration. This is another area that OSI will monitor carefully as we start reviewing these legal terms with the community.

A note about science and reproducibility

The aim of Open Source is not and has never been to enable reproducible software. The same is true for Open Source AI: reproducibility of AI science is not the objective. Open Source’s role is merely not to be an impediment to reproducibility. In other words, one can always add more requirements on top of Open Source, just like the Reproducible Builds effort does.

Open Source means giving anyone the ability to meaningfully “fork” (study and modify) a system, without requiring additional permissions, to make it more useful for themselves and also for everyone. This is why OSD #2 requires that the “source code” must be provided in the preferred form for making modifications. This way everyone has the same rights and ability to improve the system as the original developers, starting a virtuous cycle of innovation. Forking in the machine learning context has the same meaning as with software: having the ability and the rights to build a system that behaves differently than its original status. Things that a fork may achieve are: fixing security issues, improving behavior, removing bias. All these are possible thanks to the requirements of the Open Source AI Definition.

What’s coming next

With the release candidate cycle starting today, the drafting process will shift focus: no new features, only bug fixes. We’ll watch for new issues raised, watching for major flaws that may require significant rewrites to the text. The main focus will be on the accompanying documentation, the Checklist and the FAQ. We also realized that in our zeal to solve the problem of data that needs to be provided but cannot be supplied by the model owner for good reasons, we had failed to make clear the basic requirement that “if you can share the data you must.” We have already made adjustments in RC1 and will be seeking views on how to better express this in an RC2. 

In the next weeks until the 1.0 release of October 28, we’ll focus on:

• Getting more endorsers to the Definition
• Continuing to collect feedback on hackmd and forum, focusing on new, unseen-before concerns
• Preparing the artifacts necessary for the launch at All Things Open
• Iterating on the Checklist and FAQ, preparing them for deployment.

Link to the Open Source AI Definition Release Candidate 1

We asked Joseph P. De Veaugh-Geiss, a linguist and KDE’s project manager, to provide us with an explanation. If that’s not enough, we have one more argument at the end of this post. 

Why Open Source is not hyphenated

In summary:

• “open source” (no hyphen) is a lexicalized compound noun which is no longer transparent with respect to its meaning (i.e., open source is not just about being source-viewable, but also about defining user freedoms) which can then be further compounded (with for example “open source license”);
• by contrast, “open-source” (with a hyphen) is a compound modifier modifying the head noun (e.g. “intelligence”) with open having a standard dictionary meaning (i.e., “transparent” or “open to or in view of all”).

Open Source as a lexicalized compound noun

“Open source” is a lexicalized compound noun.  Although it originates with the phrase “open source software”, today “open source” is itself a unique lexeme. An example, in Red Hat’s article:

Open source has become a movement and a way of working that reaches beyond software production.

The word open in “open source” does not have the meaning “open” as one would find in the dictionary. Instead, “open source” also entails user freedoms, inasmuch as users of the software for any purpose do not have to negotiate with the rights owners to enjoy (use/improve/share/monetise) the software. That is, it is not only about transparency.

A natural example of this usage, in which the phrase open source license is clearly about more than just licensing transparency:

Because Linux is released under an open source license, which prevents restrictions on the use of the software, anyone can run, study, modify, and redistribute the source code, or even sell copies of their modified code, as long as they do so under the same license.” (from Red Hat website https://www.redhat.com/en/topics/open-source/what-is-open-source)

Note that “open source license” is itself a compound noun phrase made up of the lexicalized compound noun “open source” + the noun “license”; same for “open source movement”, etc.

What is lexicalization?

According to the Lexicon of linguistics (Utrecht University), ‘lexicalization’ is a “phenomenon by which a morphologically complex word starts to behave like an underived word in some respect, which means that at least one feature (semantic, syntactic, or phonological) becomes unpredictable”.

Underived word here means the phrase has a specific, unique meaning not (necessarily) transparent from its component parts. For instance, a “black market” is not a market which is black but rather a specific kind of market: an illegal one. A “blackboard” can be green. In other words, the entire complex phrase can be treated as a single unit of meaning stored in the mental lexicon. The meaning of the phrase is not derived using grammatical rules.

Today, the meaning of open source is unpredictable or semantically intransparent given its usage (at least by a subset of speakers) and meaning, i.e., open source is about user freedoms, not just transparency.

Other examples of lexicalized compound nouns include “yellow journalism”, “purple prose”, “dirty bomb”, “fat chance”, “green card”, “blackbird”, “greenhouse”, “high school”, etc. I tried to think of examples which are composed of adjectives + nouns but with a specific meaning not derivable by the combination of the two. I am sure you can come up with many more!

In some cases, lexicalization results in writing the compound noun phrase together as a single word (‘blackboard’), in other cases not (‘green card’). One can also build larger phrases by combining the lexicalized compound noun with another noun (e.g., black market dealer, green card holder).

Hyphenated open-source is a compound modifier

By contrast, open in “open-source intelligence” is the dictionary meaning of “open”, i.e., “open to or in view of all” or “transparent”. In this case, open-source is a compound modifier/compound adjective with a meaning comparable to “source-viewable”, “source-available”, “source-transparent”.

For compound modifiers, the hyphenation, though not obligatory, is common and can be used to disambiguate.  The presence of a head noun like “intelligence” or “journalism” is obligatory for the compound-modifier use of open-source, unlike in lexicalized compounds.

Examples of other compound modifiers + a head noun: “long-term contract”, “single-word modifier”, “high-volume printer”, etc.

Examples

There are some examples of  the compound-modifier use on Wikipedia where I think the difference between meanings lexicalized compound noun and compound modifier becomes clear:

“Open-source journalism, a close cousin to citizen journalism or participatory journalism, is a term coined in the title of a 1999 article by Andrew Leonard of Salon.com.” (from Wikipedia)

“Open-source intelligence” is intelligence “produced from publicly available information that is collected, exploited, and disseminated in a timely manner to an appropriate audience for the purpose of addressing a specific intelligence requirement” (from Wikipedia)

In these examples open-source is clearly referring to transparent, viewable-to-all sources and not to something like ‘guaranteeing user freedoms’. Moreover, my intuition for these latter examples is that removing the hyphen would change the meaning, however subtle it may be, and the change could make the original sentences incoherent (without implicit internal modification while reading):

•  “open source journalism” would refer to journalism about open source software (in sense I above), not transparent, participatory journalism;
• “open source intelligence” would refer to intelligence about open source software (in sense I above, whatever that would mean!), not intelligence from publicly available information.

The Open Source Initiative says: No hyphen!

If that explanation still doesn’t convince you, we invoke the rules of branding and “pull a Twitter”, who vandalized English with their Who To Follow : we say no hyphen!

Luckily others have already adopted the “no hyphen” camp, like the CNCF style guide. Debate closed.

If you like debates, let’s talk about capitalization: OSI in its guidelines chose to always capitalize Open Source because it is a proper noun with a specific definition. Which camp are you on?

New feature: clarified Open Source model and Open Source weights

• Under “What is Open Source AI,” there is a new paragraph that (1) identifies both models and weights/parameters as encompassed by the word “system” and (2) makes it clear that all components of a larger system have to meet the standard. There is a new sentence in the paragraph after the “share” bullet making this point.
• Under the heading “Open Source models and Open Source weights,” there is a description of the components for both of those for machine learning systems. We also edited the paragraph below those additions to eliminate some redundancy.

Training data in the preferred form to make modifications

The role of training data is one of the most hotly debated parts of the definition. After long deliberation and co-design sessions we have concluded that defining training data as a benefit, not a requirement, is the best way to go.

Training data is valuable to study AI systems: to understand the biases that have been learned, which can impact system behavior. But training data is not part of the preferred form for making modifications to an existing AI system. The insights and correlations in that data have already been learned.

Data can be hard to share. Laws that permit training on data often limit the resharing of that same data to protect copyright or other interests. Privacy rules also give a person the rightful ability to control their most sensitive information, such as decisions about their health. Similarly, much of the world’s Indigenous knowledge is protected through mechanisms that are not compatible with later-developed frameworks for rights exclusivity and sharing.

• Open training data  (data that can be reshared) provides the best way to enable users to study the system, along with the preferred form of making modifications.
• Public training data  (data that others can inspect as long as it remains available) also enables users to study the work, along with the preferred form.
• Unshareable non-public training data  (data that cannot be shared for explainable reasons) gives the ability to study some of the systems biases and demands a detailed description of the data – what it is, how it was collected, its characteristics, and so on – so that users can understand the biases and categorization underlying the system.

OSI believes these extra requirements for data beyond the preferred form of making modifications to the AI system both advance openness in all the components of the preferred form of modifying the AI system and drive more Open Source AI in private-first areas such as healthcare.

Other changes

• The Checklist is separated into its own document. This is to separate the discussion about how to identify Open Source AI from the establishment of general principles in the Definition. The content of the Checklist has also been fully aligned with the Model Openness Framework (MOF), allowing for an easy overlay.
• Under “Preferred form to make modifications,” the word “Model” changed to “Weights.” The word “Model”  was referring only to parameters, and was inconsistent with how the word “model” is used in the rest of the document.
• There is an explicit reference to the intended recipients of the four freedoms: developers, deployers and end users of AI systems.
• Incorporated credit to the Free Software Definition.
• Added references to conditions of availability of components, referencing the Open Source Definition.

Next steps

• Continue iterating through drafts after meeting diverse stakeholders at the worldwide roadshow, collect feedback and carefully look for new arguments in dissenting opinions. 
• Decide how to best address the reviews of new licenses for datasets, documentation and the agreements governing model parameters.
• Keep improving the FAQ.
• Prepare for post-stable-release: Establish a process to review future versions of the Open Source AI Definition.

Collecting input and endorsements

We will be taking draft v.0.0.9 on the road collecting input and endorsements, thanks to a grant by the Sloan Foundation. The lively conversation about the role of data in building and modifying AI systems will continue at multiple conferences from around the world, the weekly town halls and online throughout the Open Source community. 

The first two stops are in Asia: Hong Kong for AI_dev August 21-23, then Beijing for Open Source Congress August 25-27. Other events are planned to take place in Africa, South America, Europe and North America. These are all steps toward the conclusion of the co-design process that will result in the release of the stable version of the Definition in October at All Things Open. 

Creating an Open Source AI Definition is an arduous task over the past two years, but we know the importance of creating this standard so the freedoms to use, study, share and modify AI systems can be guaranteed. Those are the core tenets of Open Source, and it warrants the dedicated work it has required. You can read about the people who have played key roles in bringing the Definition to life in our Voices of Open Source AI Definition on the blog. 

How to get involved

The OSAID co-design process is open to everyone interested in collaborating. There are many ways to get involved:

• Join the forum: share your comment on the drafts.
• Leave comment on the latest draft: provide precise feedback on the text of the latest draft.
• Follow the weekly recaps: subscribe to our monthly newsletter and blog to be kept up-to-date.
• Join the town hall meetings: we’re increasing the frequency to weekly meetings where you can learn more, ask questions and share your thoughts.
• Join the workshops and scheduled conferences: meet the OSI and other participants at in-person events around the world.