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The Open Source Initiative (OSI) is running a blog series to introduce some of the people who have been actively involved in the Open Source AI Definition (OSAID) co-design process. The co-design methodology allows for the integration of diverging perspectives into one just, cohesive and feasible standard. Support and contribution from a significant and broad group of stakeholders is imperative to the Open Source process and is proven to bring diverse issues to light, deliver swift outputs and garner community buy-in.

This series features the voices of the volunteers who have helped shape and are shaping the Definition.

Meet Rahmat Akintola

What’s your background related to Open Source and AI?

Sure. I’ll start with Open Source. My journey began at PyCon Africa in 2019, where I participated in a hackathon on Cookiecutter. At the time, I had just transitioned into web development, and I was looking for ways to improve my skills beyond personal projects. So, I joined the Cookiecutter Academy at Python Africa in 2019. That’s how I got introduced to Open Source.

Since then, I’ve been contributing regularly, starting with one-off contributions to different projects. These days, I primarily focus on code and documentation contributions, mainly in web development.

As for AI, my journey started with data science. I had been working as a program manager and was part of the Women in Machine Learning and Data Science community in Accra, which was looking for volunteers. Coincidentally, I had lost my job at the time, so I applied for the program manager role and got it. That experience sparked my interest in AI. I started learning more about machine learning and AI, and I needed to build my domain knowledge to help with my role in the community.

I’ve worked on traditional models like linear and logistic regression through various courses. Recently, as part of our community, we organized a “Mathematics for Machine Learning” boot camp, where we worked on projects related to reinforcement learning and logistic regression. One dataset I worked with involved predicting BP (blood pressure) levels in the US. The task was to assess the risk of developing hypertension based on various factors.

What motivated you to join this co-design process to define Open Source AI?

The Open Source AI journey started when I was informed about a virtual co-design process that was reaching out to different communities, including mine. As the program lead, I saw it as an opportunity to merge my two passions—Open Source and AI.

I volunteered and worked on testing the OpenCV workbook, as I was using OpenCV at the time. I participated in the first phase, which focused on determining whether certain datasets needed to be open. Unfortunately, I couldn’t participate in the validation phase because I was involved in the mathematics boot camp, but I followed the discussions closely.

When the opportunity came up to participate in the co-design process, I saw it as a chance to bridge my work in Open Source web development and my growing interest in AI. It felt like the perfect moment. I was already using OpenCV, which happened to be part of the AI systems under review, so I jumped right in.

Through the process, I realized that defining Open Source AI goes beyond just using tools or making code contributions—it involves a deep understanding of data, legality, and the broader system.

How did you get invited to speak at the Deep Learning Indaba conference in Dakar? How was the conference experience? Did you make any meaningful connections?

As for speaking at Deep Learning Indaba, the opportunity came unexpectedly. One day, Mer Joyce (the OSAID co-design organizer) sent an email offering a chance to speak on Open Source AI at the conference. I had previously applied to attend but didn’t get in, so I jumped on this opportunity. We used a presentation similar to one May had given at Open Source Community Africa.

I made excellent connections. The conference itself was amazing—though the food and the Senegal experience also played a part! There were many AI and machine learning researchers, and I learned new concepts, like using JAX, which was introduced as an alternative to some common frameworks. The tutorials were well-targeted at beginners, which was perfect for me.

On a personal level, it was great to connect with academics. I’m considering applying for a master’s or Ph.D., and the conference provided an opportunity to ask questions and receive guidance.

Why do you think AI should be Open Source?

AI is becoming a significant part of our lives. I work with the Meltwater Entrepreneurial School of Technology (MEST) as a technical lead, and we use AI for various training purposes. Opening up parts of AI systems allows others to adapt and refine them to suit their needs, especially in localized contexts. For example, I saw someone on Twitter excited about building a GPT for dating, customizing it to ask specific questions.

This ability for people to tweak and refine AI models, even without building them from scratch, is important. Open-sourcing AI enables more innovation and helps tailor models for specific needs, which is why I believe it should be open to an extent.

Has your personal definition of Open Source AI changed along the way? What new perspectives or ideas did you encounter while participating in the co-design process?

One new perspective I gained was on the legal and data availability aspects of AI. Before this, I had never really considered the legal side of things, but during the co-design process, it became clear that these elements are crucial in defining Open Source AI systems. It’s more than just contributing code—it’s about ensuring compliance with legal frameworks and making sure data is available and usable.

What do you think the primary benefit will be once there is a clear definition of Open Source AI?

A clear definition would help people understand that Open Source AI involves more than just attaching an MIT or Apache license to a project on GitHub. There’s more complexity around sharing models, data and parameters.

For instance, I was once asked whether using an “Open Source” large language model like LLaMA meant the data had to be open too. A well-defined standard would provide guidance for questions like these, ensuring people understand the legal and technical aspects of making their AI systems Open Source.

What do you think are the next steps for the community involved in Open Source AI?

In Africa, I think the next step is spreading awareness about the Open Source AI Definition. Many people are still unaware of the complexities, and there’s still a tendency to assume that adding an Open Source license to a project automatically makes it open. Building collaborations with local communities to share this information is important.

For women, especially in Africa, visibility is key. When women see others doing similar work, they feel encouraged to join. Representation and community engagement play significant roles in driving diversity in Open Source AI.

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.

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