This past week, I attended a presentation by Nick Vidal from the Open Source Initiative (OSI) on Open Source AI. Going in, I had some assumptions about what open-source AI meant, but the talk really challenged my perspective, especially regarding its role in industries like finance and the ethical challenges it faces.

Eye-Opening Takeaways

One of the biggest surprises for me was just how much transparency is required for an AI system to truly be considered open source. It’s not just about making the code available–true open-source AI also needs to include the training data, model weights, and even the code used to generate the model. That’s a far higher bar than traditional open-source software, where just having the source code is enough.

Another topic that stood out was “openwashing.” I hadn’t really considered how companies might mislead people by claiming their AI is open source when, in reality, they withhold crucial components like training data. OSI is actively working to combat this, setting clear definitions to ensure that open-source AI remains genuinely open.

Open Source AI in the Financial World

Before the presentation, I wasn’t sure if open-source AI had a real place in finance, given how regulated the industry is. But my perspective changed. The financial sector already relies heavily on open-source software for things like trading algorithms, fraud detection, and risk management. While there are challenges in making AI fully open source in this space–particularly around compliance and data privacy–it can actually improve transparency in automated decision-making systems.

Rethinking Open Source AI

The discussion also made me rethink some of the challenges with open-source AI. For one, the sheer cost of training AI models means that even if the code and data are open, access to computing resources remains a huge barrier. This raises the question: Can open-source AI truly democratize AI development, or will it still be dominated by big players with deep pockets?

Another thing that stood out was the issue of transparency. Even if an AI model is fully open-source, understanding how it actually makes decisions isn’t always straightforward. Deep learning models, in particular, can be black boxes. OSI is considering whether additional transparency measures–like fairness metrics or feature attributions–should be required. That could be a step toward making AI more accountable and preventing biased models from being used irresponsibly.

The Challenges of Open Source AI

There are some big hurdles to making AI truly open source, including:

Accessibility: Not everyone has the resources to train or run large AI models. The open-source community needs to work on making infrastructure more accessible.

Funding & Sustainability: Open-source AI isn’t just about releasing code–it also requires maintaining datasets, computing power, and responsible usage. Sustainable funding models are crucial.

Legal & Compliance Issues: Especially in industries like finance and healthcare, regulations can make open-sourcing AI complicated. Developers need to navigate these legal challenges carefully.

Where Open Source AI Is Headed

Looking ahead, it’s clear that OSI’s Open Source AI Definition will need to keep evolving. Issues like reproducibility, ethical AI use, and accessibility will continue to be debated. There’s also a growing need for collaboration between open-source communities, governments, and industry leaders to make sure AI remains open while being used responsibly.The conversation around open-source AI is still evolving, and it’s going to be interesting to see where it goes from here.