LLM-gen-AI | Justin Wheeler

What does AI Alignment mean in open source?

Tue, 15 Sep 2026 08:00:00 +0000

In July, I shared an update about my new role as AI Alignment Community Architect at Red Hat, focused on Fedora. This post clarifies what that role entails, why "AI alignment" is more than a technical term, and how I plan to support the Fedora community in leveraging LLM-gen-AI.

I organized this blog post into three sections:

Reclaiming AI Alignment: The meaning behind the term.
Model builder engagement: Why do we need bidirectional feedback loops?
My work in Fedora: Upcoming priorities this quarter and the path forward.

Note	I use the term “LLM-gen-AI” throughout this article aligned to the Software Freedom Conservancy’s recommendations. This is in support of addressing this technology in community-first terms.

Reclaiming AI Alignment: The meaning behind a term

As I defined my new role, I carefully considered the title. The Fedora community sentiment toward LLM-gen-AI is deeply divided: some rally against it, while others push for rapid adoption without wider community consensus. I needed a title that signaled a neutral, balanced approach. My mandate at Red Hat is to support upstream projects in adopting AI, focused primarily on Fedora. Working in the “AI” space at Red Hat is fascinating because I am exposed to diverse ways that customers use and deploy innovative open source technology. Additionally, Red Hat provides real value by supporting customers in their “hybrid AI” journeys. Many of my Red Hat colleagues consistently push for more Free Software and open source answers for customers and enterprises building infrastructure to support AI inference, local models, and more. Red Hat has a responsibility to innovate when new technology opportunities emerge that its customers are acting upon. With this in mind, I am more convinced that LLM-gen-AI is something that open source maintainers and contributors can leverage for real workloads. There are opportunities to solve real problems and routine maintenance tasks for complex projects. LLM-gen-AI used right can support maintainers in automating boring, cyclical work so they can focus more on the exciting work of innovation and focused engineering efforts within their projects. Or even going outside and spending time offline.

However, I distinguish "AI alignment" from "AI adoption." "AI adoption" communicates a pre-defined, non-negotiable stance where the goal is simply to increase usage. "AI alignment," by contrast, communicates that LLM-gen-AI use exists on a spectrum.

My intent in Fedora is not to insist, but to negotiate and compromise. I want to align how our community uses these tools with our existing values, norms, and culture.

CHAOSS AI Alignment Working Group & model builders

My approach to "AI alignment" is influenced by the CHAOSS Project. I co-chair the CHAOSS AI Alignment Working Group with Emma Irwin and Coraline Ada Ehmke. Recently, Emma, Adrian Edwards, and I presented at FOSSY 2026 on this topic in greater detail. This experience frames my definition of "alignment" as I move forward in my new role.

Traditionally, "AI alignment" is a term used by model builders to describe processes where communities have little influence. As LLM-gen-AI grows in widespread use, the power gap between those model builders and the communities they impact will widen. This creates an unsustainable dynamic in the safety and well-being of our communities with these new tools.

We need more than just "AI adoption". We need bidirectional feedback loops. Free Software communities deserve a seat at the table. This is not just for iterating on model development, but for defining how we integrate LLM-gen-AI tools sustainably and responsibly.

To achieve this, we (i.e., open source community citizens) need a stronger value proposition for engagement with model builders. Whether commercial or altruistic, we must persuade model builders that a community-driven approach is a critical advantage. If we refuse to engage, we risk Free Software values and culture being shut out of conversations entirely. So, I believe it is better to be an advisor than a bystander. Advising is its own form of open source contribution. Therefore, I lend my support toward the wider notion that "AI alignment" fosters two-way conversations that ensure model builders actually listen to the communities their work impacts.

My work in Fedora: What I hope to work on next

September 2026 is my first full month in this role. While there is still much to define, a few priorities have emerged. Here is where I will be focusing my initial energy:

Migrating "This Week in Fedora": Aurélien Bompard (@abompard) created a useful tool for AI-curated, human-reviewed weekly summaries. Currently, it lives on a personal fedorapeople.org space. I am beginning to work with Aurélien to migrate this to a weekly WordPress article on the Fedora Community Blog, since we first began talking about this in July. I am already submitting pull requests to support this transition.
Launching LLM-gen-AI Agent Skills: Together with the AI/ML SIG, we are building the Fedora Agent Skills Library. These define best practices for using LLM-gen-AI agents to automate routine maintenance. My immediate task is community architecture: setting up the repository for contributions and improving documentation so these skills are accessible and scalable.
AI/ML SIG Documentation: As the Fedora Docs Team works to identify "team captains" for specific topics, I am volunteering to lead AI/ML SIG documentation. This involves importing or deprecating content in the Quick Docs site and migrating extensive Wiki documentation to the Fedora Docs site, creating a central, discoverable home for all things LLM-gen-AI in Fedora.
An update to the Fedora AI-Assisted Contributions Policy?: Honestly, I am not sure about this one yet. But it seems apparent to me that eleven months after the Fedora Council first introduced the Fedora AI-Assisted Contributions Policy, it is time for an update. Nearly a full year of lived experience has happened within the framework of this lightweight policy. Furthermore, a coalition of Fedora contributors agree that an update is needed, but there is not a single, shared view of what those updates should be. It will take more community input and feedback to shape the next iteration to that policy. I anticipate facilitating inclusive future community conversations about what those changes should be.

I am both excited and nervous to work with fellow Fedorans on these initiatives. I know there are strongly-held opinions on both sides of the LLM-gen-AI debate. (An understatement!) I accepted this role because I believe participation is more constructive than standing on the sidelines. My goal is to navigate this work in alignment with Fedora’s Four Foundations: Freedom, Friends, Features, First.

Until next time!

Since March 2026, I invested a lot of time into migrating my blog to a new publishing system and improving the website user interface. However, now that my site is fully functioning on a technical level, it is refreshing to begin writing content again here. There is more to come from me in this space. Expect new content about my work in Fedora and the LLM-gen-AI space, and other open source and personal items too.

The Fedora AI Developer Desktop Debate: Reflections from Red Hat Summit 2026

Tue, 19 May 2026 08:00:00 +0000

I attended Red Hat Summit 2026 in Atlanta last week from May 11-13. As always, the keynotes and product announcements were interesting, but the real value of the event happened in the "hallway track." The 1:1 conversations over coffee or meals provide the blunt truth about how people actually feel, especially regarding heated community topics like the proposed Fedora AI Developer Desktop.

Over breakfast one morning, I asked three different people a simple question. If you could change one thing about Fedora governance or the project right now, what would it be? I received three entirely different answers. While this question was not specifically about the Fedora AI Developer Desktop, it provided an opportunity to hear governance grievances and think aloud together about what fixing these things may look like. With the spirit of collaboration and transparency in mind and respecting the Chatham House Rule, I want to share those concerns and what I believe they mean for our immediate future.

1. The AI Developer Desktop: The Weight of the "-1" and the Demand for Consensus

The first piece of feedback came from a community member who expressed deep exhaustion over the recent Fedora AI Developer Desktop proposal. They highlighted a structural imbalance: the feeling that all appointed roles on the Fedora Council wield too much executive authority, leaving elected Council representatives feeling powerless to stand up against what may appear like thorny, political pushes.

This is not just an abstract fear. I heard from past and present Council members who privately expressed reservations about a proposal, yet felt immense pressure to vote "yes" publicly. A strong desire for unanimous consent inadvertently coerces people into agreement, even when core concerns remain unaddressed.

This dynamic is part of why I cast a -1 vote on the Fedora AI Developer Desktop initiative, even after having initially voted +1 in favor. I did not take the decision to change my vote lightly, but the fact that I changed my vote to pump the brakes validates the feedback I have heard informally.

If you look at the Fedora Council charter, we operate on a consensus decision-making model designed to foster collaboration and seek agreement. A -1 vote does not act as a hostile veto or a permanent denial. By definition, it is a mechanism that "immediately halts the process and requires discussion." It triggers a mandatory cooling-off period. When we move too fast, risking the alienation of our core engineering and kernel experts, we must utilize the tools our charter gives us to buy time, address specific concerns, and ensure actual, rather than theatrical, consensus. Burning bridges in any community is far easier than rebuilding them.

Perhaps there is room to evolve the Fedora Council governance and decision-making model to leave more room for non-blocking dissent. There is definitely room to consider how the Fedora Council invites community feedback into difficult conversations, in a way that feels like we are listening instead of dictating.

2. The Vision Void Surrounding the AI Developer Desktop

The second insight came from a long-time Fedora contributor. This contributor participates in Fedora as part of their job, but their heart always prioritizes the community. The feedback was simple: our overall messaging is muddled, and we fail to tell a coherent story about what we produce. This was not specific to any individual output or deliverable, such as the Fedora AI Developer Desktop, but rather as a whole in terms of how we promote what Fedora is and is not.

This symptom points to a larger issue: we currently lack a cohesive, strategic vision. We build more deliverables than ever, yet new users find it increasingly confusing to know where to begin. It reminds me of the pre-2014 era of Fedora, before Matthew Miller led the Fedora.next strategy. Back then, it felt like the message about what Fedora Linux was unclear. We had several downloads available, all promoted equally. It was confusing for end-users about where to start consuming Fedora, and which things were more polished and which things were more experimental. So, the creation of Fedora Editions gave us a crisp, defined story about our work and impact. It provided the narrative and structure to better explain the most critical, most important parts of what we produce as a community. Today, in 2026, perhaps we find ourselves lost in the weeds again.

Since coming into the role last year, the current Fedora Project Leader, Jef Spaleta, has spent a lot of time thinking about vision. The world changed a lot in the last few years, and there are parts of Fedora which are struggling to keep up. The insight shared with me by the Fedora contributor in this section reminded me of the ideas that Jef has voiced about what a new vision for Fedora could look like. What comes after Fedora.next? I do not envy him because it is a huge challenge to address, yet it is critical for the future of the project. I believe that Jef has the makings of a vision that could rally the community for the next decade of our work. Perhaps we, as Fedora Council, could help rally around the ideas for a new vision and surface this in the community. As a member of the Fedora governance, I would be happy to work together with Jef and other Fedora community leaders to have the wider conversation.

In time, I hope we can address this insight shared by the Fedora contributor I spoke with. Ultimately, we cannot align on a strategy we have not yet read. I am optimistic that we will build something together with the community for the next decade of Fedora.

3. Trademarks, Transparency, and an AI Developer Desktop Remix

The final piece of feedback came from an EPEL maintainer with deep community roots. They expressed frustration over Red Hat using the Fedora trademark in major product announcements without clear community visibility.

Effective Fedora governance requires modernized trademark guidelines and a commitment to transparency. I propose creating a public trademark ledger. If a trademark authorization requires an embargo for a corporate announcement, we can accommodate that. It would be unwise for the future of Fedora to turn down opportunities to work together with partners on announcements that require this initial secrecy. However, any such embargo must carry a strict expiration date, after which we publish the authorization to the public ledger.

This brings us back to the Fedora AI Developer Desktop. We need clear branding boundaries, and we may already have the perfect tool for this: the Fedora Remix.

The Fedora AI Developer Desktop could be an excellent candidate for a new Fedora Remix. Whether it operates as a formal Community Initiative or not, the Fedora Remix model gives the team driving the work the liberty to take risks, try new ideas, and include necessary proprietary bits (like Nvidia CUDA) without forcing them to follow the strict, high bar of official Fedora deliverables right out of the gate. Taking this path prevents the alienation of our core contributor base while still allowing innovation to happen. If it succeeds and sustains a community, we can always formalize it later.

Moving Forward from Red Hat Summit 2026

First, I am grateful for the candid, honest feedback given to me when I asked an open-ended question to these three community members. I emphasized each person to be honest and to think big, if they could really change anything but only a single thing. I admired the thoughtfulness each person gave to their answer, even if we all acknowledged most of these challenges did not have any "easy fix". This goes to say, Fedora remains strong because our contributors care enough to have these hard conversations. So, let’s use the tools we have (e.g., charter-driven cooling-off periods, transparent public visions, and the Remix model) to build a future that respects the Four Foundations that Fedora is built on.

Why the Fedora AI-Assisted Contributions Policy Matters for Open Source

Mon, 04 May 2026 08:00:00 +0000

The intersection of open source development and artificial intelligence (AI) is currently a minefield of valid excitement and legitimate anxiety. Recently, the Fedora Project introduced the Fedora AI-Assisted Contributions Policy. The full policy text outlines the basic ground rules. I was informally involved in the drafting process. Since its rollout, I am actively adjusting my own workflow to comply with its requirements.

For those outside the Fedora ecosystem, the policy establishes basic ground rules for how AI can be used in the project. It operates on three main pillars:

Accountability: You can use AI, but you own the output. The human contributor is always the author and fully accountable for the submission’s quality, license compliance, and utility.
Transparency: If a significant part of a contribution is taken unchanged from an AI tool, you must disclose it (typically via an Assisted-by: commit trailer).
Evaluation Limits: AI cannot act as the final judge on substantive contributions or evaluate a person’s standing within the community.

As someone who relies on these tools, I want to share my perspective on why this policy matters, the harsh realities of enforcing it, and why we must tactically embrace AI to protect the future of software freedom.

The "Accountability" Reality and the Threat of "Slop"

On paper, the Accountability clause looks like a strong deterrent against low-quality, automated spam. In reality, it functions primarily as a legal liability shield. If a contributor breaks a system by submitting AI-generated falsehoods, the policy simply puts the fault at their feet.

It does not, however, stop the spam.

We saw this clearly during a recent round of the Outreachy internship program. We had a record number of contributions recorded, but very few were actually merged. The volume of noise and sloppy contributions ballooned compared to past rounds. Many newcomers ignored the transparency mandate and flooded our git repositories with low-quality contributions. What actually deterred this "AI slop" wasn’t the text of the policy; it was the tangible enforcement mechanism of internship eligibility.

The primary consequence of submitting AI spam isn’t legal prosecution. It is reputational damage.

The Ethical Elephant in the Room

This brings us to the loudest objection from the open source community: the ethics of AI generation. Many maintainers deeply feel that LLMs are fundamentally engaged in theft. Maintainers recognize that the companies behind the LLMs extracted immense value from 40+ years of open source material without contributing value back, bypassing licenses and author attribution entirely.

I sympathize with the maintainers who hold this objection. It is frustrating to watch value extracted without reciprocity. However, I do not share the same sense of existential panic as other maintainers.

My view goes that the old rules simply no longer apply. Open source projects must evolve to survive. Instead of playing defense and fighting a philosophical war we cannot win, we need to think about how to tactically use AI to advance the interests of software freedom in a time when it has never been more threatened.

The Fedora AI-Assisted Contributions Policy Dual Mandate

When used responsibly, AI has the potential to be a massive equalizer. It can lower the barrier to entry for non-native English speakers drafting documentation and help junior developers navigate legacy codebases with decades or more of context. Democratizing access is how we attract the next generation of open source contributors and strengthen the social fabric of our community. This includes Fedora!

But there is a dark side to this democratization. Dumping a massive volume of low-effort, AI-facilitated contributions onto the laps of Fedora maintainers is a recipe for disaster. Many Fedora contributors are already stretched thin, doing more than is required of them. Forcing them to review a tidal wave of "AI slop" breeds deep resentment; to them, it feels like the active "enshittification" of our own community.

This is why we cannot focus solely on the people side of AI inclusion. We must carefully balance it by providing maintainers with the resources, support, and AI-facilitated tooling they need to survive. Practical improvements need deeper research and evaluation, such as using AI for advanced automated testing, scaling review workflows, and complex code refactoring. We should use AI to improve the quality and efficiency of our community’s work, ensuring that the same technology that lowers the barrier to entry also helps maintainers manage the gates.

If we handle this correctly, AI won’t replace the community. It will empower a more inclusive, diverse, and heavily-fortified one. The Fedora AI-Assisted Contributions Policy is our first step. Review the official Fedora Project policy to see the framework in action. Let’s keep the process open, keep the declarations honest, and tactically build the future of open source together.

FOSDEM 2020, pt. 2: Can Free Software include ethical AI systems?

Tue, 21 Apr 2020 00:00:00 +0000

This post is a follow-up to FOSDEM 2020, pt. 1: Play by play. This post summarizes the talk given by me and my colleague, Mike Nolan, at FOSDEM 2020.

FOSDEM 2020 took place from Saturday, 1 February, 2020 to Sunday, 2 February, 2020 in Brussels, Belgium (shortly after Sustain OSS 2020 and CHAOSScon EU 2020). On Saturday, together with my colleague and friend Mike Nolan, we presented on a topic he and I have co-conspired on for the last six months. What are the intersections of Free Software and artificial intelligence (AI)?

What is a rights-based approach for designing minimally safe and transparent guidelines for AI systems? In this talk, we explore what a Free AI system might look like. Then, taking research and guidelines from organizations such as Google and the UN Office for the Coordination of Humanitarian Affairs, we propose practical policies and tools to ensure those building an AI system respect user freedom. Lastly, we propose the outlines of a new kind of framework where all derivative works also respect those freedoms.

Freedom and AI: Can Free Software include ethical AI systems? Exploring the intersection of Free software and AI

Video recording from FOSDEM 2020

This post is an abridged summary of the key ideas and thoughts Mike and I presented at our FOSDEM 2020 session.

Let’s frame this first. 🔗

The idea behind this talk was a seed planted at MozFest 2018. I remember sitting with Mike in a hotel lobby while he was clocking some remote hours for work. We had some honest conversation about the experiences we’ve had across the public, private, and humanitarian aid sectors. At the time, Mike worked at the International Rescue Committee, and I had just finished a six-month internship with the UNICEF Office of Innovation working on open source community management.

The best way I can think of to describe humanitarian work is that it comes with the highest of highs and lowest of lows. Finding success usually means either a direct or indirect impact on some of the most vulnerable populations on our planet. Making mistakes or seeing planned efforts spin out in the wrong direction mean the resources your organization do not have the full impact they could, or sometimes worse, they fix one problem and create another.

At MozFest 2018, the seeds of thought for the intersections of Free Software as it relates to humanitarian aid and artificial intelligence were planted. Fast-forward a year to October 2019. Mike and I caught up after a while and after we had both had new experiences out in the industry. We began tending to the seed planted the year before. Our venting sessions and simultaneous cheers turned from our video chats back to the wider Free Software ecosystem.

The FOSDEM 2020 call for papers would open in a few more weeks from when we first began talking about these ideas in 2019. FOSDEM 2020 is a great collection of many different minds and people in the Free Software world, so it felt like the perfect audience for us to deliver our thoughts to an audience for the first time.

We applied, and later found out our application to the main track was accepted, landing us in the Community and Ethics track on Saturday.

This blog post is a partial recap of what we covered in that FOSDEM 2020 session linked above. The FOSDEM 2020 talk covered three main ideas. This blog post summarizes only the definitions of our AI Freedoms.

Looking to yesterday for today 🔗

Before we define AI freedoms, we need to define the origin of Free Software. While the precise origin is debatable, Mike and I posit that the Free Software movement began with a definition of rights. These freedoms were first established by the GNU Project on 27 Sept. 1983.

So, in the spirit of the movement that precedes us today, Mike and I started asking questions. As we go into a new decade, what would Freedoms of AI look like? We know we aren’t presenting The Truth or The Answer. But we are presenting our experiences and reflections.

What freedoms do we want to protect? 🔗

So, Free Software has the Four Freedoms. Combining our shared history in the Free Software movement with our personal experiences working in both commercial and humanitarian contexts, this is our vision of Three possible Freedoms of AI:

1. Audit automated decision-making systems 🔗

We are entitled to know and understand how decisions that affect us are made. So, you should have to freedom to audit, or understand, how those automated decisions are made.

For a moment, picture yourself in a classroom. Your teacher gives you a bad grade on an essay. You are probably curious to know why you received the grade that you did, especially if it was something important to you. You would likely go to the teacher and ask for help understanding why you received the grade you did, if there were no comments or notes.

So, why wouldn’t you want to understand how a decision is made that affects you?

This does tie closely to one of the Four Freedoms of Software: the freedom to “study the source”. This AI freedom restates that Software freedom as, “the freedom to understand how a system that is impacting you works”. But this does go beyond the Software freedom to study the source because it is more than just source code.

2. Deliver accountability and responsibility 🔗

We already know AI systems are capable of harm. We deserve the guarantee of liability when these systems do create harm. Not if, when. So, you should also have the freedom to deliver and expect accountability and responsibility from the designers of automated decision-making systems that impact you.

Think about it for a minute. When machines make decisions for us, who is accountable for those decisions? Is it the machines? Is it the creators who programmed them? Or is it the end-user?

It should be the creators and organizations that create these systems, as they directly profit from their output. Those who create these systems typically do so to profit themselves.

But we as those affected deserve to be put over their profit.

Myanmar and Facebook 🔗

What would a real world example of this look like? Have you ever thought about how social media could be connected to genocidal campaigns? This is what the United Nations is asking of Facebook in the role of the news feed in inciting genocide. But why would the U.N. ask these questions of Facebook?

The problem identified with Facebook is the news feed. The news feed optimizes for “engaging content”. To understand this, we have to define what “engaging content” means. Studies have shown that optimizing for “engagement” increases recommendations for extremist and alarmist content. Researchers knew the potential of “engaging content” to be misused, years before the Rohingya genocide began in Myanmar.

Then, the warnings in research became real. Religious and military leaders created “fake news” to divide the citizens of Myanmar. This “fake news” campaign is suspected to be a major contributing factor of the ethnic cleaning of the Rohingya people in Myanmar.

Obviously Facebook didn’t take genocide into account in their feature planning when building the news feed. Yet it was still a contributing factor. And because of the research and warnings delivered to Facebook before the genocide, they did know about the potential for misuse.

So, who is responsible? Facebook? Myanmar religious and military leaders? Nobody? Even today, nobody will take responsibility. But what we do know is that profit was placed over people.

Thus, we must demand for a freedom that delivers accountability and responsibility in a world increasingly defined and shaped by automated decision-making. We are already in a “catch-up” mode to the changing world in this regard.

3. Appeal a decision 🔗

No decision-making system is ever perfect. We are always missing some data. So, lastly, you should also have the freedom to appeal a decision that impacts you.

In your life, have you ever told a story to help someone empathize with your situation? Have you ever had to explain a misconstrued fact about your background or history? You are probably familiar with the phrase, “walk in someone else’s shoes”. Our ability to empathize and understand is what connects us as humans. It helps avert disaster more often than you think.

There are always hidden stories not captured by a set of data points. Thus, we should also have the opportunity to break through automated systems that influence an organization. We deserve the opportunity to appeal to the people behind the systems in order to use our humanity.

Resume/CV filtering 🔗

If you have ever applied for a job, you might be familiar with the importance of keywords and certain requirements on your resume or C.V. Automated tools already exist to review these documents and filter out candidates.

But what is the point of interviews? Where you talk with a real person? Interviews are a chance to tell our own hidden stories and explain the gap between what is on our CV and what is not. It gives us a chance to build empathy between us and who we want to be our employer.

Practical examples 🔗

It is not hard to look for examples of appealing systems in our society. Two examples are appellate courts and credit denials in the U.S.

Appellate courts are legal systems where we appeal decisions we think were unfair or incorrect. Sometimes this is because of an unfair trial. It could also be because of a biased judge. Either way, a system is in place to appeal a legal decision.

A similar system exists in the U.S. if you are denied a credit offer or loan. You can appeal to an impartial third-party if you believe a credit offer or loan was denied to you on unfair criteria. again, there is a system in place to appeal a decision that could have significant personal impact to you.

How else could we describe the freedom to appeal? We must not erase the opportunity for human connection and empathy when these decisions are made. Even by automated systems. To do so is to deny our humanity.

Where do we go from here? 🔗

The stakes have never been higher. These systems are being built not next year, not next month, not tomorrow, but now. These problems are not going to go away. They are only going to continue and grow. But we know we can imagine and have a better world, because we have to. The alternative is simply unacceptable.

The history of the Free Software movement left us clues about how to build a social movement to address problematic patterns in our digital society. So, let’s take the cues and demand for our Freedoms to be respected, just like those who came before us nearly 40 years ago.

If there is anything to take away from this post, or the original talk, it is this: me, you, and everyone has some power to make change. So, let’s build a future that we own, together, for everyone.

For more details and ideas on how we as individuals can protect these Freedoms and how to get others to respect these Freedoms, watch the full talk!

How did Free Software build a social movement?

Tue, 14 Apr 2020 00:00:00 +0000

The Free Software movement is rooted to origins in the 1980s. As part of a talk I gave with my colleague and friend Mike Nolan at FOSDEM 2020, we analyzed how the Free Software movement emerged as a response to a changing digital world in three different phases. This blog post is an exploration and framing of that history to understand how the social movement we call “Free Software” was constructed.

Why does this matter? 🔗

This exploration and thought experiment is important to understand when revisiting social movements in technology in the current day. In the FOSDEM 2020 talk Mike and I gave, we presented three possible digital “freedoms” for artificial intelligence. The rights-based approach we presented at FOSDEM 2020 was inspired by the origin of the Free Software movement.

But to understand how we got to today with thousands of contributors to the Linux project, billions of dollars in open source company buyouts, and the words “open source” used on mainstream cable news channels, we have to start from the beginning, in 1983.

27 Sept. 1983: GNU Project announced 🔗

On 27 September 1983, the GNU Project was announced by Richard Stallman. The GNU Project was a collection of Free Software tools for building a free operating system. But it was also more than that. The GNU Project came with a vision to give computer users freedom and control of their use of computers. To do this, the GNU Project advocated for four fundamental freedoms:

Run software in any way desired
Copy and distribute the software
Study it (i.e. reading the source code)
Modify it and make changes

Today, we call these the Four Freedoms.

So, the GNU Project was founded with these fundamental freedoms as the motivation for why they did what they did. It was more than shipping code for code’s sake, but to lead by example in how software could be developed without sacrificing the rights of users.

4 Oct. 1985: Free Software Foundation founded 🔗

Next, skip ahead to 4 October 1985. Two years after the launch of GNU, the Free Software Foundation (F.S.F.) is founded to support and sustain GNU and the Free Software movement. The values of the GNU Project were important and valuable, but it wasn’t enough to leave them out in the world on their own.

At first, the F.S.F. focused on employing software developers to work on Free Software and the GNU Project. Later, the F.S.F. transitioned to legal and structural issues to support the Free Software community.

So, it is one thing to have your values and ethics out there, but they need to be protected and respected by the rest of the world too. The F.S.F. represented the sustainability of protecting these rights and beliefs, originally put forth by GNU.

While the F.S.F. does help sustain those rights, how does a nonprofit foundation actually enforce these rights in practice?

25 Feb. 1989: GNU General Public License created 🔗

Finally, we skip ahead four more years to 25 February 1989: the first version of the GNU Public License (G.P.L.) is created. This is the license that gave “copyleft” a name. It was written and released for the GNU Project, but the license itself was stewarded by the F.S.F.

The G.P.L. put power in the hands of individual people and activists to shape how others used their software. Thus, copyleft is put into a practical legal policy. In a sense, the G.P.L. allowed software developers to place the Four Freedoms at the core of their code.

Although enforcement of copyleft licenses has a blemished history, it was still the “teeth” in translating these values and values to the rest of the world. It took inspiration from how copyright was not something often considered when distributing software until the early 1980s.

And thus, copyleft becomes a radical invention in software with the proliferation of the G.P.L., especially in its adoption in prominent projects like the Linux kernel.

So this was a lot of history. Is the past relevant to where we are today? First, consider how the early Free Software movement responded to these emerging societal issues in the 1980s.

Free Software was a response to the changing ecosystem of software distribution. Software became more valued because of a standardization on hardware that didn’t exist previously. There were simply fewer architectures to compile for!

Suddenly, the value of software increased. It became a commodity.

Before this commodification of software, the Four Freedoms were, in a sense, the default way of distributing and sharing software. After commodification, this was no longer true. The Four Freedoms were rooted in a belief that there are essential rights that belong to all users of computers and computer systems. Stallman observed this change directly at the MIT Media Lab in the 1970s and early 1980s. This motivated him and many others to stand up for Software Freedom by asserting these freedoms.

To respond to commodification of software, Free Software took a freedom-based approach to established their values, as the Four Freedoms. So, looking back 40 years ago, is it possible to extend and make the past relevant again in today’s changing world?

Before we can answer that, we have to first ask. How has the world changed?

Your future is the new commodity. 🔗

The history of Free Software overlaps with what is happening now.

Today the world is about predictions: predictions about human futures. This is accomplished by the combination of software and data. Human futures are a simple formula: Data + Software. Or, artificial intelligence and machine learning.

But how are human futures becoming a commodity? In the 1980s, software became the thing we “sold”. It had inherent value. Today, the ability to predict what you are doing to do next is valuable. This makes both your and my future the new commodity. Where will we go next? What will we buy next? Who have we contacted recently?

But data is only one piece of this big puzzle. It is the enabling force for determining our futures. Third-party organizations collect the world’s data on a massive, centralized scale. Your data is what allows companies to sell your future.

To add a metaphor, data is like oil, not gold. You consume the input (data) to sell the output (human futures).

Where are we today? 🔗

So, how have we responded to our changing world?

There have been some successful resistance to the new value of user data and human futures. The privacy movement and legislation like G.D.P.R. are representative of this.

However, data privacy is only one part of the big picture. Focusing on individual empowerment does not protect us from societal effects. Consider predictive policing and court rulings as two examples.

Ultimately, the data privacy movement has been a key factor in combating the effects of surveillance capitalism, but there are still gaps. Mike and I noticed we need to approach topics like artificial intelligence not in pieces, but as a whole.

And some organizations have recognized this challenge and are working to address it. “Working groups” and reports with non-mandatory recommendations are on the rise. However, these groups are not effective on moving forward ways of ensuring people are effectively protected from the unforeseen harms of AI systems. “Light self-regulation” works on an opt-in model, and it is against the interest of some actors to opt in.

So, if we are in the middle of this societal shift from software as a commodity to human futures as a commodity, where do we go from here? Do we choose chaos or community?

At time of publication, I am still wrestling with these questions. As are a lot of people! To get a wider picture of what is on my mind in 2020, read my event reports from my pre-coronavirus 2020 travels.

Photo by Shane Rounce on Unsplash.