Open-source software helps influence the development of AI and its policies.

Open-source software quietly affects nearly every issue in AI policy, but it is largely absent from discussions around AI policy—policymakers need to more actively consider OSS’s role in AI.

Open-source software (OSS), software that is free to access, use, and change without restrictions, plays a central role in the development and use of artificial intelligence (AI). Across open-source programming languages such as Python, R, C++, Java, Scala, Javascript, Julia, and others, there are thousands of implementations of machine learning algorithms. OSS frameworks for machine learning, including tidymodels in R and Scikit-learn in Python, have helped consolidate many diverse algorithms into a consistent machine learning process and enabled far easier use for the everyday data scientist. There are also OSS tools specific to the especially important subfield of deep learning, which is dominated by Google’s Tensorflow and Facebook’s PyTorch. Manipulation and analysis of big data (data sets too large for a single computer) were also revolutionized by OSS, first by the Hadoop ecosystem and later by projects like Spark. These are not simply some of the AI tools—they are the best AI tools. While proprietary data analysis software may sometimes enable machine learning without the need to write code, it does not enable analytics that are as well developed as those in modern OSS.

That the most advanced tools for machine learning are largely free and publicly available matters for policymakers, and thus the OSS world deserves more attention. The United States government has gotten better at supporting OSS broadly, notably through the Federal Source Code Policy, which encourages agencies to release more of the code they write and procure. Yet the relationship between OSS and AI policy goes less acknowledged. Trump administration documents on AI regulation and using AI at federal agencies mention OSS only in passing. The Obama administration’s AI strategy notes the important role of OSS in AI innovation, but does not mention its relevance to other issues. A new European Parliament report states that European OSS policies lack “a clear link to the AI policies and strategies… for most countries.” In fact, the recent proposed European AI regulation does not address the role of OSS at all.

Generally speaking, analyses and international comparisons of AI capacity often include talent, funding, data, semiconductors, and compute access, but often lack a discussion of the role of OSS. This is an unfortunate oversight since OSS quietly affects nearly every issue in AI policy. AI tools built in OSS enable the faster adoption of AI in science and industry, while also speeding proliferation of ethical AI practices. At the same time, OSS is playing a complex role in markets—powering innovation in many areas, while also further empowering Google and Facebook and challenging the traditional role of standards bodies.